Difference between revisions of "BioNLP 2023"

From ACL Wiki
Jump to navigation Jump to search
(Created page with "==BIONLP 2014 1st Call for Papers== An ACL 2014 2-day Workshop associated with the SIGBIOMED special interest group Featuring a special track on NLP approaches for assessment...")
 
m (Dina moved page BioNLP Workshop to BioNLP 2023: preserve the last workshop under the original organizers )
 
(301 intermediate revisions by 2 users not shown)
Line 1: Line 1:
==BIONLP 2014 1st Call for Papers==
+
[[SIGBIOMED]]
  
An ACL 2014 2-day Workshop associated with the SIGBIOMED special interest group Featuring a special track on NLP approaches for assessment of clinical conditions and a panel on shared tasks.
+
<font size="4"><b>BIONLP 2023 and Shared Tasks @ ACL 2023</b></font>
 +
 
 +
<!-- START -->
 +
The 22nd BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with [https://2023.aclweb.org/ ACL 2023]
 +
 
 +
 
 +
===IMPORTANT DATES===
 +
 
 +
* April 24, 2023: Workshop Paper Due Date.
 +
* Submission site for the workshop only: https://softconf.com/acl2023/BioNLP2023/
 +
* Submission site for the SHARED TASKS only:  https://softconf.com/acl2023/BioNLP2023-ST
 +
<!-- *Submission site: https://www.softconf.com/acl2022/BioNLP2022 -->
 +
* May 29, 20232: Notification of Acceptance
 +
* June 6, 2023: Camera-ready papers due
 +
* June 12, 2023: Pre-recorded video due
 +
 
 +
Video is optional. Instructions (below) are for the video only, not for the final paper submission. Video should not exceed 10 minutes.
 +
 
 +
Instructions:
 +
  https://docs.google.com/presentation/d/1STKSZ22v3ucS9smfDfhREQhwRB9_bIwu7mnVYKUq7A8/edit?usp=sharing
 +
 
 +
Form (linked in SLIDE 4)
 +
https://acl2023workshops.paperform.co/
 +
 
 +
 
 +
* <b>BioNLP 2023</b> Workshop at ACL, July 13, 2023, Toronto, Canada
 +
 
 +
 
 +
Registration: https://2023.aclweb.org/registration/
 +
 
 +
===VISA Information===
 +
ACL organizers are processing the requests.
 +
 
 +
Please see the instructions here: https://2023.aclweb.org/blog/visa-info/
 +
 
 +
 
 +
===Poster size: ===
 +
All posters should be A0, orientation: Portrait.
 +
 
 +
 
 +
 
 +
===BioNLP 2023: Program===
 +
<p style="font-size: 20px"><b>Thursday July 13, 2023</b></p>
 +
<table cellspacing="1" cellpadding="5" border="0">
 +
 
 +
<tr><td colspan=2>Location: Pier 2 Ballroom</td></tr>
 +
<tr><td valign=top>8:30&#8211;8:40</td><td valign=top><b> Opening remarks</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 1: Evaluating speech, models and literature-related tasks</b></td></tr>
 +
<tr><td valign=top width=100>8:40&#8211;9:00</td><td valign=top align=left><i>Evaluating and Improving Automatic Speech Recognition using Severity</i><br>
 +
Ryan Whetten and Casey Kennington, <i>Boise State University</i></td></tr>
 +
<tr><td valign=top width=100>9:00&#8211;9:20</td><td valign=top align=left><i>Is the ranking of PubMed similar articles good enough? An evaluation of text similarity methods for three datasets</i><br>
 +
Mariana Neves, Ines Schadock, Beryl Eusemann, Gilbert Schönfelder, Bettina Bert, Daniel Butzke, <i>German Federal Institute for Risk Assessment</i></td></tr>
 +
<tr><td valign=top width=100>9:20&#8211;9:40</td><td valign=top align=left><i>BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition (Online)</i><br>
 +
Vera Pavlova and Mohammed Makhlouf, <i>rttl.ai</i></td></tr>
 +
<tr><td valign=top width=100>9:40&#8211;10:00</td><td valign=top align=left><i>Promoting Fairness in Classification of Quality of Medical Evidence/i><br>Simon Suster<sup>1</sup>, Timothy Baldwin<sup>2</sup>, Karin Verspoor<sup>3</sup>, <i><sup>1</sup>University of Melbourne, <sup>2</sup>MBZUAI, <sup>3</sup>RMIT University</i></td></tr>
 +
<tr><td valign=top width=100>10:00&#8211;10:30</td><td valign=top align=left><i>BioLaySumm 2023 Shared Task: Lay Summarisation of Biomedical Research Articles</i><br>
 +
Tomas Goldsack<sup>1</sup>, Zheheng Luo<sup>2</sup>, Qianqian Xie<sup>2</sup>, Carolina Scarton<sup>1</sup>, Matthew Shardlow<sup>3</sup>, Sophia Ananiadou<sup>2</sup>, Chenghua Lin<sup>1</sup>, <i>
 +
<sup>1</sup>University of Sheffield, <sup>2</sup>University of Manchester, <sup>3</sup>Manchester Metropolitan University/i></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>10:30&#8211;11:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 2: Clinical Language Processing</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>11:00&#8211;11:40</b></td><td valign=top style="padding-top: 14px;"><b>Invited Talk: <i>Dementia Detection from Speech: New Developments and Future Directions</i> <br> Speaker:  Kathleen Fraser</b></td></tr>
 +
<tr><td valign=top width=100>11:40&#8211;12:10</td><td valign=top align=left><i>Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes</i><br>
 +
Yanjun Gao<sup>1</sup>, Dmitriy Dligach<sup>2</sup>, Timothy Miller<sup>3</sup>, Majid Afshar<sup>1</sup>, <i>
 +
<sup>1</sup>University of Wisconsin, <sup>2</sup>Loyola University Chicago, <sup>3</sup>Boston Children's Hospital and Harvard Medical School</i></td></tr>
 +
<tr><td valign=top width=100>12:10&#8211;12:40</td><td valign=top align=left><i>Overview of the RadSum23 Shared Task on Multi-modal and Multi-anatomical Radiology Report Summarization</i><br>
 +
Jean-Benoit Delbrouck, Maya Varma, Pierre Chambon, Curtis Langlotz, <i>Stanford University</i></td></tr>
 +
<tr><td valign=top width=100>12:40&#8211;13:00</td><td valign=top align=left><i>RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models</i><br>
 +
Dave Van Veen<sup>1</sup>, Cara Van Uden<sup>1</sup>, Maayane Attias<sup>1</sup>, Anuj Pareek<sup>1</sup>, Christian Bluethgen<sup>1</sup>, Malgorzata Polacin<sup>2</sup>, Wah Chiu<sup>1</sup>, Jean-Benoit Delbrouck<sup>1</sup>, Juan Zambrano Chaves<sup>1</sup>, Curtis Langlotz<sup>1</sup>, Akshay Chaudhari<sup>1</sup>, John Pauly<sup>1</sup>, <i>
 +
<sup>1</sup>Stanford University, <sup>2</sup>Stanford University, ETH Zurich</i></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>13:00&#8211;14:30</b></td><td valign=top style="padding-top: 14px;"><b><em>Lunch</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:00&#8211;17:45</b></td><td valign=top style="padding-top: 14px;"><b>Onsite Poster Session 1</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>How Much do Knowledge Graphs Impact Transformer Models for Extracting Biomedical Events?</i><br>
 +
Laura Zanella and Yannick Toussaint, <i>LORIA, Université de Lorraine</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>DISTANT: Distantly Supervised Entity Span Detection and Classification</i><br>
 +
Ken Yano<sup>1</sup>, Makoto Miwa<sup>2</sup>, Sophia Ananiadou<sup>3</sup>, <i>
 +
<sup>1</sup>The National Institute of Advanced Industrial Science and Technology, <sup>2</sup>Toyota Technological Institute, <sup>3</sup>University of Manchester</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Event-independent temporal positioning: application to French clinical text</i><br>
 +
Nesrine Bannour<sup>1</sup>, Bastien Rance<sup>2</sup>, Xavier Tannier<sup>3</sup>, Aurélie Névéol<sup>1</sup>, <i>
 +
<sup>1</sup>Université Paris Saclay, CNRS, LISN, <sup>2</sup>INSERM, centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Paris Cité, AP-HP, HEGP, HeKa, Inria Paris, <sup>3</sup>Sorbonne Université, Inserm, LIMICS</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>AliBERT: A Pre-trained Language Model for French Biomedical Text</i><br>
 +
Aman Berhe<sup>1</sup>, Guillaume Draznieks<sup>2</sup>, Vincent Martenot<sup>2</sup>, Valentin Masdeu<sup>2</sup>, Lucas Davy<sup>2</sup>, Jean-Daniel Zucker<sup>3</sup>, <i>
 +
<sup>1</sup>SU/IRD UMMISCO & Quinten, <sup>2</sup>Quinten, <sup>3</sup>SU/IRD, UMMISCO</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Building a Corpus for Biomedical Relation Extraction of Species Mentions</i><br>
 +
Oumaima El Khettari, Solen Quiniou, Samuel Chaffron, <i>Nantes Université - LS2N</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges</i><br>
 +
Gilchan Park<sup>1</sup>, Byung-Jun Yoon<sup>1</sup>, Xihaier Luo<sup>1</sup>, Vanessa López-Marrero<sup>1</sup>, Patrick Johnstone<sup>1</sup>, Shinjae Yoo<sup>2</sup>, Francis Alexander<sup>1</sup>, <i> <sup>1</sup>Brookhaven National Laboratory, <sup>2</sup>BNL
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge</i><br>
 +
François Remy, Kris Demuynck, Thomas Demeester, <i>Ghent University - imec</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Resolving Elliptical Compounds in German Medical Text</i><br>
 +
Niklas Kämmer<sup>1</sup>, Florian Borchert<sup>1</sup>, Silvia Winkler<sup>1</sup>, Gerard de Melo<sup>2</sup>, Matthieu-P. Schapranow<sup>1</sup>, <i><sup>1</sup>Hasso Plattner Institute, University of Potsdam, <sup>2</sup>HPI/University of Potsdam</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>End-to-end clinical temporal information extraction with multi-head attention</i><br>
 +
Timothy Miller<sup>1</sup>, Steven Bethard<sup>2</sup>, Dmitriy Dligach<sup>3</sup>, Guergana Savova<sup>1</sup>, <i> <sup>1</sup>Boston Children's Hospital and Harvard Medical School, <sup>2</sup>University of Arizona, <sup>3</sup>Loyola University Chicago</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Intermediate Domain Finetuning for Weakly Supervised Domain-adaptive Clinical NER</i><br>
 +
Shilpa Suresh, Nazgol Tavabi, Shahriar Golchin, Leah Gilreath, Rafael Garcia-Andujar, Alexander Kim, Joseph Murray, Blake Bacevich, Ata Kiapour, <i>Musculoskeletal Informatics Group, Boston Children's Hospital, Harvard Medical School</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Biomedical Language Models are Robust to Sub-optimal Tokenization</i><br>
 +
Bernal Jimenez Gutierrez, Huan Sun, Yu Su, <I>The Ohio State University</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>BioNART: A Biomedical Non-AutoRegressive Transformer for Natural Language Generation</i><br>
 +
Masaki Asada<sup>1</sup> and Makoto Miwa<sup>2</sup>, <i>
 +
<sup>1</sup>National Institute of Advanced Industrial Science and Technology, <sup>2</sup>Toyota Technological Institute</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Can Social Media Inform Dietary Approaches for Health Management? A Dataset and Benchmark for Low-Carb Diet</i><br>
 +
Skyler Zou, Xiang Dai, Grant Brinkworth, Pennie Taylor, Sarvnaz Karimi, <i>CSIRO</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Hospital Discharge Summarization Data Provenance</i><br>
 +
Paul Landes<sup>1</sup>, Aaron Chaise<sup>2</sup>, Kunal Patel<sup>1</sup>, Sean Huang<sup>2</sup>, Barbara Di Eugenio<sup>1</sup>, <i>
 +
<sup>1</sup>University of Illinois at Chicago, <sup>2</sup>Vanderbilt University</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers</i><br>
 +
Israt Jahan<sup>1</sup>, Md Tahmid Rahman Laskar<sup>2</sup>, Chun Peng<sup>1</sup>, Jimmy Huang<sup>1</sup>, <i>
 +
<sup>1</sup>York University, <sup>2</sup>Dialpad Inc.</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Zero-Shot Information Extraction for Clinical Meta-Analysis using Large Language Models</i><br>
 +
David Kartchner<sup>1,3</sup>, Selvi Ramalingam<sup>2</sup>, Irfan Al-Hussaini<sup>3</sup>, Olivia Kronick<sup>3</sup>, Cassie Mitchell<sup>3</sup>, <i><sup>1</sup>Enveda Biosciences, <sup>2</sup>Emory University, <sup>3</sup>Georgia Institute of Technology</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers</i><br>
 +
Shreya Chandrasekhar, Chieh-Yang Huang, Ting-Hao Huang, <i> Penn State University</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Extracting Drug-Drug and Protein-Protein Interactions from Text using a Continuous Update of Tree-Transformers</i><br>
 +
Sudipta Singha Roy and Robert E. Mercer, <i>The University of Western Ontario</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction</i><br>
 +
Simon Meoni<sup>1</sup>, Éric De la Clergerie<sup>2</sup>, Théo Ryffel<sup>3</sup>,<i>
 +
<sup>1</sup>Arkhn/INRIA, <sup>2</sup>Iniria, <sup>3</sup>Arkhn</i></td></tr>
 +
 
 +
 
 +
<tr><td valign=top style="padding-top: 14px;"><b>15:30&#8211;16:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
 
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:30&#8211;17:45</b></td><td valign=top style="padding-top: 14px;"><b>Virtual Session 1</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction</i><br>
 +
Yueling Li<sup>1</sup>, Sebastian Martschat<sup>1</sup>, Simone Paolo Ponzetto<sup>2</sup>, <i>
 +
<sup>1</sup>BASF SE, <sup>2</sup>University of Mannheim</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Gaussian Distributed Prototypical Network for Few-shot Genomic Variant Detection</i><br>
 +
Jiarun Cao, Niels Peek, Andrew Renehan, Sophia Ananiadou, <i> University of Manchester</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Boosting Radiology Report Generation by Infusing Comparison Prior</i><br>
 +
Sanghwan Kim<sup>1</sup>, Farhad Nooralahzadeh<sup>2</sup>, Morteza Rohanian<sup>2</sup>, Koji Fujimoto<sup>3</sup>, Mizuho Nishio<sup>3</sup>, Ryo Sakamoto<sup>3</sup>, Fabio Rinaldi<sup>4</sup>, Michael Krauthammer<sup>2</sup>, <i>
 +
<sup>1</sup>ETH Zürich, <sup>2</sup>University of Zurich, <sup>3</sup>Kyoto University Graduate School of Medicine, <sup>4</sup>IDSIA, Swiss AI Institute</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints</i><br>
 +
Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji, Vinod Kumar, Bronner P. Gonçalves, Christiana Kartsonaki, ISARIC Clinical Characterisation Group, Laura Merson, David Clifton, <i>
 +
University of Oxford</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Zero-shot Temporal Relation Extraction with ChatGPT</i><br>
 +
Chenhan Yuan, Qianqian Xie, Sophia Ananiadou, <i>University of Manchester</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media</i><br>
 +
Tianlin Zhang, Kailai Yang, Sophia Ananiadou, <i>University of Manchester</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Exploring Drug Switching in Patients: A Deep Learning-based Approach to Extract Drug Changes and Reasons from Social Media</i><br>
 +
Mourad Sarrouti, Carson Tao, Yoann Mamy Randriamihaja, <i>Sumitovant Biopharma</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports</i><br>
 +
Perceval Wajsburt<sup>1</sup> and Xavier Tannier<sup>2</sup>, <i>
 +
<sup>1</sup>Sorbonne Université, <sup>2</sup>Sorbonne Université, Inserm, LIMICS</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformers</i><br>
 +
Vinayak Arannil, Tomal Deb, Atanu Roy, <i>Amazon</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Privacy Aware Question-Answering System for Online Mental Health Risk Assessment</i><br>
 +
Prateek Chhikara, Ujjwal Pasupulety, John Marshall, Dhiraj Chaurasia, Shweta Kumari, <i>University of Southern California</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Multiple Evidence Combination for Fact-Checking of Health-Related Information</i><br>
 +
Pritam Deka, Anna Jurek-Loughrey, Deepak P, <i>Queen's University Belfast</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Comparing and combining some popular NER approaches on Biomedical tasks</i><br>
 +
Harsh Verma, Sabine Bergler, Narjesossadat Tahaei, <i>Concordia University</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health</i><br>
 +
Chandreen Liyanage<sup>1</sup>, Muskan Garg<sup>2</sup>, Vijay Mago<sup>1</sup>, Sunghwan Sohn<sup>2</sup>, <i>
 +
<sup>1</sup>Lakehead University, <sup>2</sup>Mayo Clinic</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Distantly Supervised Document-Level Biomedical Relation Extraction with Neighborhood Knowledge Graphs</i><br>
 +
Takuma Matsubara, Makoto Miwa, Yutaka Sasaki, <i>Toyota Technological Institute</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Biomedical Relation Extraction with Entity Type Markers and Relation-specific Question Answering</i><br>
 +
Koshi Yamada, Makoto Miwa, Yutaka Sasaki, <i>Toyota Technological Institute</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Biomedical Document Classification with Literature Graph Representations of Bibliographies and Entities</i><br>
 +
Ryuki Ida, Makoto Miwa, Yutaka Sasaki, <i>Toyota Technological Institute</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>WeLT: Improving Biomedical Fine-tuned Pre-trained Language Models with Cost-sensitive Learning</i><br>
 +
Ghadeer Mobasher<sup>1,2</sup>, Wolfgang Müller<sup>2</sup>, Olga Krebs<sup>2</sup>, Michael Gertz<sup>1</sup>
 +
<sup>1</sup>Heidelberg University, <sup>2</sup>Heidelberg Institute for Theoretical Studies – HITS gGmbH</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Exploring Partial Knowledge Base Inference in Biomedical Entity Linking</i><br>Hongyi Yuan<sup>1</sup>, Keming Lu<sup>2</sup>, Zheng Yuan<sup>3</sup>, <i>
 +
<sup>1</sup>Tsinghua University, <sup>2</sup>University of Southern California, <sup>3</sup>Alibaba Group</i></td></tr>
 +
 
 +
 
 +
 
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:00&#8211;17:45</b></td><td valign=top style="padding-top: 14px;"><b>Onsite Shared Task Poster Session</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>GRASUM at BioLaySumm Task 1: Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries</i><br>Domenic Rosati, <i> scite</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Team:PULSAR at ProbSum 2023:PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models</i><br>
 +
Hao Li<sup>1</sup>, Yuping Wu<sup>1</sup>, Viktor Schlegel<sup>2</sup>, Riza Batista-Navarro<sup>1</sup>, Thanh-Tung Nguyen<sup>3</sup>, Abhinav Ramesh Kashyap<sup>2</sup>, Xiao-Jun Zeng<sup>1</sup>, Daniel Beck<sup>4</sup>, Stefan Winkler<sup>5</sup>, Goran Nenadic<sup>1</sup>, <i>
 +
<sup>1</sup>University of Manchester, <sup>2</sup>ASUS AICS,  <sup>3</sup>ASUS, <sup>4</sup>University of Melbourne, <sup>5</sup>National University of Singapore</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models</i><br>
 +
Potsawee Manakul, Yassir Fathullah, Adian Liusie, Vyas Raina, Vatsal Raina, Mark Gales, <i> University of Cambridge</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation</i><br>
 +
Sanjeev Kumar Karn<sup>1</sup>, Rikhiya Ghosh<sup>2</sup>, Kusuma P<sup>2</sup>, Oladimeji Farri<sup>2</sup>, <i>
 +
<sup>1</sup>Siemens, <sup>2</sup>Siemens Healthineers</I></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models</i><br>
 +
Mong Yuan Sim<sup>1</sup>, Xiang Dai<sup>2</sup>, Maciej Rybinski<sup>3</sup>, Sarvnaz Karimi<sup>3</sup>, <i>
 +
<sup>1</sup>The University of Adelaide, <sup>2</sup>CSIRO Data61, <sup>3</sup>CSIRO</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization</i><br>
 +
Gangwoo Kim<sup>1</sup>, Hajung Kim<sup>1</sup>, Lei Ji<sup>2</sup>, Seongsu Bae<sup>3</sup>, chanhwi kim<sup>4</sup>, Mujeen Sung<sup>1</sup>, Hyunjae Kim<sup>1</sup>, Kun Yan<sup>5</sup>, Eric Chang<sup>6</sup>, Jaewoo Kang<sup>1</sup>, <i>
 +
<sup>1</sup>Korea University, <sup>2</sup>MSRA, <sup>3</sup>KAIST, <sup>4</sup>Korea University, DMIS, <sup>5</sup>Beihang University, <sup>6</sup>Kingtex</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation</i><br>
 +
Yu-Hsuan Wu, Ying-Jia Lin, Hung-Yu Kao, <i>National Cheng Kung University</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>MDC at BioLaySumm Task 1: Evaluating GPT Models for Biomedical Lay Summarization</i><br>
 +
Oisín Turbitt, Robert Bevan, Mouhamad Aboshokor, <i>Medicines Discovery Catapult</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i> LHS712EE at BioLaySumm 2023: Using BART and LED to summarize biomedical research articles</i><br>
 +
Quancheng Liu, Xiheng Ren, V.G.Vinod Vydiswaran<i>, University of Michigan</i></td></tr>
 +
 
 +
 
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:30&#8211;17:45</b></td><td valign=top style="padding-top: 14px;"><b>Virtual Shared Task Poster Session</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>TALP-UPC at ProbSum 2023: Fine-tuning and Data Augmentation Strategies for NER</i><br>
 +
Neil Torrero, Gerard Sant, Carlos Escolano, <i>Universitat politècnica de catalunya</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i> Team Converge at ProbSum 2023: Abstractive Text Summarization of Patient Progress Notes</i><br>
 +
Gaurav Kolhatkar, Aditya Paranjape, Omkar Gokhale, Dipali Kadam, <i>Pune Institute Of Computer Technology</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i> nav-nlp at RadSum23: Abstractive Summarization of Radiology Reports using BART Finetuning</i><br>
 +
Sri Macharla, Ashok Madamanchi, Nikhilesh Kancharla<i>, IIT Roorkee at Roorkee</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i> APTSumm at BioLaySumm Task 1: Biomedical Breakdown, Improving Readability by Relevancy Based Selection</i><br>
 +
A.S. Poornash, Atharva Deshmukh, Archit Sharma, Sriparna Saha<i>, Indian Institute of Technology Patna</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>ISIKSumm at BioLaySumm Task 1: BART-based Summarization System Enhanced with Bio-Entity Labels</i><br>
 +
Cağla Colak and İlknur Karadeniz, <i>Işık University</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language ModelsClinical Progress Note Summarization with Rules and Languague Models</i><br>
 +
Ming Liu<sup>1</sup>, Dan Zhang<sup>1</sup>, Weicong Tan<sup>2</sup>, He Zhang<sup>3</sup>
 +
<sup>1</sup>Deakin University, <sup>2</sup>Monash University, <sup>3</sup>CNPIEC KEXIN LTD</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>ELiRF-VRAIN at BioNLP Task 1B: Radiology Report Summarization</i><br>
 +
Vicent Ahuir Esteve, Encarna Segarra, Lluís Hurtado, <i>
 +
Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>SINAI at RadSum23: Radiology Report Summarization Based on Domain-Specific Sequence-To-Sequence Transformer Model</i><br>
 +
Mariia Chizhikova, Manuel Díaz-Galiano, L. Alfonso Ureña-López, M. Teresa Martín-Valdivia, <i>
 +
University of Jaén</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization</i><br>
 +
Jinge Wu<sup>1</sup>, Daqian Shi<sup>2</sup>, Abul Hasan<sup>1</sup>, Honghan Wu<sup>1</sup>, <i>
 +
<sup>1</sup>University College London, <sup>2</sup>University of Trento</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation</i><br>
 +
Aaron Nicolson, Jason Dowling, Bevan Koopman, <i>CSIRO</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>UTSA-NLP at RadSum23: Multi-modal Retrieval-Based Chest X-Ray Report Summarization</i><br>
 +
Tongnian Wang, Xingmeng Zhao, Anthony Rios<i>, University of Texas at San Antonio</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>VBD-NLP at BioLaySumm Task 1: Explicit and Implicit Key Information Selection for Lay Summarization on Biomedical Long Documents</i><br>
 +
Phuc Phan, Tri Tran, Hai-Long Trieu, <i>VinBigData, JSC</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models</i><br>
 +
Chao-Yi Chen, Jen-Hao Yang, Lung-Hao Lee, <i>National Central University</i></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Pathology Dynamics at BioLaySumm: the trade-off between Readability, Relevance, and Factuality in Lay Summarization</i><br>
 +
Irfan Al-Hussaini, Austin Wu, Cassie Mitchell, <i>Georgia Institute of Technology</i></td></tr>
 +
 
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>IITR at BioLaySumm Task 1:Lay Summarization of BioMedical articles using Transformers</i><br>
 +
Venkat praneeth Reddy, Pinnapu Reddy Harshavardhan Reddy, Karanam Sai Sumedh, Raksha Sharma, <i>Indian Institute of Technology,Roorkee</i></td></tr>
 +
 
 +
<tr><td valign=top width=100>'''17:45-18:00'''</td> <td><b>Closing remarks</b></td></tr>
 +
 
 +
</table>
 +
 
 +
===BioNLP 2023 Invited Talk===
 +
 
 +
Title: Dementia Detection from Speech: New Developments and Future Directions
 +
 +
 
 +
Abstract: Diagnosing and treating dementia is a pressing concern as the global population ages. A growing number of publications in NLP tackle the question of whether we can use speech and language analysis to automatically detect signs of this devastating disease. However, the field of NLP has changed rapidly since the task was first proposed. In this talk, Dr. Kathleen Fraser will summarize the foundational approaches to dementia detection from speech, and then review how current approaches are building on and improving over the earlier work. Dr. Fraser will present several areas that she believes are promising future directions, and discuss preliminary work from her group specifically on the topic of multimodal machine learning for remote cognitive assessment.
 +
 +
Bio: Dr. Kathleen Fraser is a computer scientist in the Digital Technologies Research Centre at the National Research Council Canada.  Her research focuses on the use of natural language processing (NLP) in healthcare applications, as well as assessing and mitigating social bias in artificial intelligence systems. Dr. Fraser received her PhD in computer science from the University of Toronto in 2016, and subsequently completed a post-doc at the University of Gothenburg, Sweden. She was named an MIT Rising Star in Electrical Engineering and Computer Science, and was awarded the Governor General's Gold Academic Medal in 2017. She also co-founded the start-up Winterlight Labs, later acquired by Cambridge Cognition. She has been a research officer at the National Research Council since 2018 and also holds a position as adjunct professor at Carleton University.
 +
 +
 
 +
 
 +
===WORKSHOP OVERVIEW AND SCOPE===
 +
 
 +
The BioNLP workshop associated with the ACL SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. The workshop is running every year since 2002 and continues getting stronger.
 +
BioNLP welcomes and encourages work on languages other than English, and inclusion and diversity. BioNLP truly encompasses the breadth of the domain and brings together researchers in bio- and clinical NLP from all over the world. The workshop will continue presenting work on a broad and interesting range of topics in NLP.
 +
The interest to biomedical language has broadened significantly due to the COVID-19 pandemic and continues to grow: as access to information becomes easier and more people generate and access health-related text, it becomes clearer that only language technologies can enable and support adequate use of the biomedical text.
 +
 
 +
BioNLP 2023 will be particularly interested in language processing that supports DEIA (Diversity, Equity, Inclusion and Accessibility). The work on detection and mitigation of bias and misinformation continues to be of interest. Research in languages other than English, particularly, under-represented languages, and health disparities are always of interest to BioNLP.
 +
 
 +
Other active areas of research include, but are not limited to:
 +
* Tangible results of biomedical language processing applications;
 +
* Entity identification and normalization (linking) for a broad range of semantic categories;
 +
* Extraction of complex relations and events;
 +
* Discourse analysis;
 +
* Anaphora/coreference resolution;
 +
* Text mining / Literature based discovery;
 +
* Summarization;
 +
* Τext simplification;
 +
* Question Answering;
 +
* Resources and strategies for system testing and evaluation;
 +
* Infrastructures and pre-trained language models for biomedical NLP (Processing and annotation platforms);
 +
* Development of synthetic data & data augmentation;
 +
* Translating NLP research into practice;
 +
* Getting reproducible results.
 +
 
 +
 
 +
===SUBMISSION INSTRUCTIONS===
 +
 
 +
Two types of submissions are invited: full (long) papers and short papers.
 +
 
 +
Submission site for the workshop only: https://softconf.com/acl2023/BioNLP2023/
 +
 
 +
Shared task participants' reports should be submitted at  https://softconf.com/acl2023/BioNLP2023-ST.
 +
 
 +
The reports on the shared task participation will be reviewed by the task organizers.
 +
 
 +
Publication chairs for the tasks:
 +
* 1A: Yanjun Gao
 +
* 1B: Jean Benoit Delbrouck
 +
* 2: Chenghua Lin, Tomas Goldsack
 +
 
 +
Full (long) papers should not exceed eight (8) pages of text, plus unlimited references.
 +
Final versions of full papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account.
 +
Full papers are intended to be reports of original research.
 +
 
 +
BioNLP aims to be the forum for interesting, innovative, and promising work involving biomedicine and language technology, whether or not yielding high performance at the moment.
 +
This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation. 
 +
Both types of research and combinations thereof are encouraged. 
 +
 
 +
Short papers may consist of up to four (4) pages of content, plus unlimited references.
 +
Upon acceptance, short papers will still be given up to five (5) content pages in the proceedings.
 +
Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
 +
 
 +
 
 +
====Electronic Submission====
 +
Submissions must be electronic and in PDF format, using the Softconf START conference management system at https://softconf.com/acl2023/BioNLP2023/
 +
 
 +
We strongly recommend consulting the ACL Policies for Submission, Review, and Citation: https://2023.aclweb.org/calls/main_conference/ and using ACL LaTeX style files tailored for this year's conference. Submissions must conform to the official style guidelines: https://2023.aclweb.org/calls/style_and_formatting/
 +
 
 +
<b>Submissions need to be anonymous.</b>
 +
 
 +
<b>Dual submission policy:</b> papers may NOT be submitted to the BioNLP 2023 workshop if they are or will be concurrently submitted to another meeting or publication.
 +
 
 +
===Program Committee===
 +
 
 +
  * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
 +
  * Emilia Apostolova, Anthem, Inc., USA
 +
  * Eiji Aramaki, University of Tokyo, Japan
 +
  * Saadullah Amin, Saarland University, Germany
 +
  * Steven Bethard, University of Arizona, USA
 +
  * Olivier Bodenreider, US National Library of Medicine
 +
  * Robert Bossy, Inrae, Université Paris Saclay, France
 +
  * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain
 +
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA
 +
  * Brian Connolly, Ohio, USA
 +
  * Mike Conway, University of Melbourne, Australia
 +
  * Manirupa Das, Amazon, USA
 +
  * Berry de Bruijn, National Research Council, Canada
 +
  * Dina Demner-Fushman, US National Library of Medicine
 +
  * Bart Desmet, National Institutes of Health, USA
 +
  * Dmitriy Dligach, Loyola University Chicago, USA
 +
  * Kathleen C. Fraser, National Research Council Canada
 +
  * Travis Goodwin, Amazon Web Services (AWS), Seattle, Washington, USA
 +
  * Natalia Grabar, CNRS, U Lille, France
 +
  * Cyril Grouin, Université Paris-Saclay, CNRS
 +
  * Tudor Groza, EMBL-EBI
 +
  * Deepak Gupta, US National Library of Medicine
 +
  * William Hogan, UCSD, USA
 +
  * Thierry Hamon, LIMSI-CNRS, France
 +
  * Richard Jackson, AstraZeneca
 +
  * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 +
  * Sarvnaz Karimi, CSIRO, Australia
 +
  * Nazmul Kazi, University of North Florida, USA
 +
  * Roman Klinger, University of Stuttgart, Germany
 +
  * Anna Koroleva, Omdena
 +
  * Majid Latifi, Department of Computer Science, University of York, York, UK
 +
  * Andre Lamurias, Aalborg University, Denmark
 +
  * Alberto Lavelli, FBK-ICT, Italy
 +
  * Robert Leaman, US National Library of Medicine
 +
  * Lung-Hao Lee, National Central University, Taiwan
 +
  * Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany
 +
  * Timothy Miller, Boston Childrens Hospital and Harvard Medical School, USA
 +
  * Claire Nedellec, French national institute of agronomy (INRA)
 +
  * Guenter Neumann, German Research Center for Artificial Intelligence (DFKI)
 +
  * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany
 +
  * Nhung Nguyen, National Centre for Text Mining, University of Manchester, UK
 +
  * Aurélie Névéol, CNRS, France
 +
  * Amandalynne Paullada, University of Washington School of Medicine
 +
  * Yifan Peng,  Weill Cornell Medical College, USA
 +
  * Laura Plaza, Universidad Nacional de Educación a Distancia
 +
  * Francisco J. Ribadas-Pena, University of Vigo, Spain
 +
  * Anthony Rios, The University of Texas at San Antonio, USA
 +
  * Kirk Roberts, The University of Texas Health Science Center at Houston, USA
 +
  * Roland Roller, DFKI, Germany
 +
  * Mourad Sarrouti, Sumitovant Biopharma, Inc., USA
 +
  * Diana Sousa, University of Lisbon, Portugal
 +
  * Peng Su, University of Delaware, USA
 +
  * Madhumita Sushil, University of California, San Francisco, USA
 +
  * Mario Sänger, Humboldt Universität zu Berlin, Germany
 +
  * Andrew Taylor, Yale University School of Medicine, USA
 +
  * Karin Verspoor, RMIT University, Australia
 +
  * Leon Weber, Humboldt Universität Berlin, Germany
 +
  * Nathan M. White, James Cook University, Australia
 +
  * Dustin Wright, University of Copenhagen,Denmark
 +
  * Amelie Wührl,  University of Stuttgart, Germany
 +
  * Dongfang Xu, Harvard University, USA
 +
  * Jingqing Zhang,  Imperial College London, UK
 +
  * Ayah Zirikly, Johns Hopkins Whiting School of Engineering, USA
 +
  * Pierre Zweigenbaum, LIMSI - CNRS, France
 +
 
 +
 
 +
====Organizers====
 +
 +
  * Dina Demner-Fushman, US National Library of Medicine
 +
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine
 +
  * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
 +
  * Jun-ichi Tsujii, National Institute of Advanced Industrial Science and Technology, Japan
 +
 
 +
===SHARED TASKS 2023===
 +
 
 +
Shared Tasks on Summarization of Clinical Notes and Scientific Articles
 +
 
 +
The first task focuses on Clinical Text.
 +
 
 +
<h5>Task 1A. Problem List Summarization</h5>
 +
 
 +
<b>Codalab competition for Problem List Summarization Evaluation: https://codalab.lisn.upsaclay.fr/competitions/12388
 +
Test Set Release: https://physionet.org/content/bionlp-workshop-2023-task-1a/1.1.0/  </b>
 +
 +
 
 +
<b> The deadline for registration is March 1st, after which no further registrations will be accepted.</b>
 +
 
 +
Automatically summarizing patients’ main problems from the daily care notes in the electronic health record can help mitigate information and cognitive overload for clinicians and provide augmented intelligence via computerized diagnostic decision support at the bedside. The task of Problem List Summarization aims to generate a list of diagnoses and problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization.This task aims to promote NLP model development for downstream applications in diagnostic decision support systems that could improve efficiency and reduce diagnostic errors in hospitals. This task will contain 768 hospital daily progress notes and 2783 diagnoses in the training set, and a new set of 300 daily progress notes will be annotated by physicians as the test set. The annotation methods and annotation quality have previously been reported [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354726/ here]. The goal of this shared task is to attract future research efforts in building NLP models for real-world decision support applications, where a system generating relevant and accurate diagnoses will assist the healthcare providers’ decision-making process and improve the quality of care for patients.
 +
 
 +
 +
<b>Shared Task 1A Registration</b>: https://forms.gle/yp6TKD66G8KGpweN9
 +
 
 +
Please join our Google discussion group for the important update: https://groups.google.com/g/bionlp2023problemsumm
 +
 
 +
<h6>Full Task 1A Details at:</h6> https://physionet.org/content/bionlp-workshop-2023-task-1a/1.0.0/
 +
 +
 +
Important Dates:
 +
* <s> Registration Started: January 13th, 2023 </s>
 +
* <s> Releasing of training and validation data: January 13th, 2023 </s>
 +
* <s> Registration stops: March 1, 2023 </s>
 +
* Releasing of test data: April 13th, 2023
 +
 
 +
Codalab competition for Problem List Summarization Evaluation: https://codalab.lisn.upsaclay.fr/competitions/12388
 +
Test Set Release: https://physionet.org/content/bionlp-workshop-2023-task-1a/1.1.0/ 
 +
 +
* <b> System submission deadline: April 20th, 2023 </b>
 +
* System papers due date:  April 28th, 2023
 +
* Notification of acceptance: June 1st, 2023
 +
* Camera-ready system papers due: June 6, 2023
 +
* BioNLP Workshop Date: July 13th, 2023
 +
 +
 
 +
<b>Task 1A Organizers:</b>
 +
* Majid Afshar, Department of Medicine University of Wisconsin - Madison.
 +
* Yanjun Gao, University of Wisconsin Madison.
 +
* Dmitriy Dligach, Department of Computer Science at Loyola University Chicago.
 +
* Timothy Miller, Boston Children’s Hospital and Harvard Medical School.
 +
 
 +
<h5>Task 1B. Radiology report summarization</h5>
 +
 
 +
Radiology report summarization is a growing area of research. Given the Findings and/or Background sections of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study.
 +
 
 +
The research area of radiology report summarization currently faces an important limitation: most research is carried out on chest X-rays. To palliate these limitations, we propose two datasets:
 +
A shared summarization task that includes six different modalities and anatomies, totalling 79,779 samples, based on the MIMIC-III database.
 +
 
 +
A shared summarization task on chest x-ray radiology reports with images and a brand new out-of-domain test-set from Stanford.
 +
 
 +
<h6>Full Task 1B details at:</h6>  https://vilmedic.app/misc/bionlp23/sharedtask
 +
 
 +
<b>Task 1B Organizers:</b>
 +
* Jean-Benoit Delbrouck, Stanford University.
 +
* Maya Varma, Stanford University.
 +
 
 +
 
 +
<h5>Task 2. Lay Summarization of Biomedical Research Articles </h5>
 +
Biomedical publications contain the latest research on prominent health-related topics, ranging from common illnesses to global pandemics. This can often result in their content being of interest to a wide variety of audiences including researchers, medical professionals, journalists, and even members of the public. However, the highly technical and specialist language used within such articles typically makes it difficult for non-expert audiences to understand their contents.
 +
 
 +
Abstractive summarization models can be used to generate a concise summary of an article, capturing its salient point using words and sentences that aren’t used in the original text. As such, these models have the potential to help broaden access to highly technical documents when trained to generate summaries that are more readable, containing more background information and less technical terminology (i.e., a “lay summary”).
 +
 
 +
This shared task surrounds the abstractive summarization of biomedical research articles, with an emphasis on controllability and catering to non-expert audiences. Through this task, we aim to help foster increased research interest in controllable summarization that helps broaden access to technical texts and progress toward more usable abstractive summarization models in the biomedical domain.
 +
 
 +
<h6>For more information on Task 2, see:</h6>
 +
* Main site: https://biolaysumm.org/
 +
* CodaLab page - subtask 1: https://codalab.lisn.upsaclay.fr/competitions/9541
 +
* CodaLab page - subtask 2: https://codalab.lisn.upsaclay.fr/competitions/9544
 +
 
 +
Detailed descriptions of the motivation, the tasks, and the data are also published in:
 +
 
 +
* Goldsack, T., Zhang, Z., Lin, C., Scarton, C.. Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature. EMNLP 2022.
 +
* Luo, Z., Xie, Q., Ananiadou, S.. Readability Controllable Biomedical Document Summarization. EMNLP 2022 Findings.
 +
 
 +
 
 +
<b>Task 2 Organizers:</b>
 +
* Chenghua Lin, Deputy Director of Research and Innovation in the Computer Science Department, University of Sheffield.
 +
* Sophia Ananiadou, Turing Fellow, Director of the National Centre for Text Mining and Deputy Director of the Institute of Data Science and AI at the University of Manchester.
 +
* Carolina Scarton, Computer Science Department at the University of Sheffield.
 +
* Qianqian Xie, National Centre for Text Mining (NaCTeM).
 +
* Tomas Goldsack, University of Sheffield.
 +
* Zheheng Luo, the University of Manchester.
 +
* Zhihao Zhang, Beihang University.
 +
 
 +
 
 +
 
 +
<!--
 +
 
 +
=== MEDIQA 2019 === 
 +
 
 +
A BioNLP-19 shared task on textual inference and question entailment
 +
 
 +
In 2019, the workshop will present the results of the shared task on biomedical textual inference and question entailment.
 +
See details at https://sites.google.com/view/mediqa2019
 +
 
 +
-->
 +
 
 +
<!--  <font size="4"><b>BIONLP 2018</b></font>
 +
 
 +
An ACL 2018 Workshop associated with the SIGBIOMED special interest group, Melbourne, Australia, Thursday July 19, 2018 
 +
 
 +
 
 +
===IMPORTANT DATES===
 +
 
 +
*Submission deadline: Monday April 16, 2018 11:59 PM Eastern US
 +
*Notification of acceptance: <s>Monday May 21</s>, Thursday, May 24, 2018 (Sorry for the delay)
 +
*Camera-ready copy due from authors: <s> Monday May 28</s>, Thursday, May 31, 2018
 +
*'''Workshop: Thursday July 19, 2018'''
 +
 
 +
 
 +
<h2>BioNLP 2018 WORKSHOP PROGRAM</h2>
 +
 
 +
<table cellspacing="0" cellpadding="5" border="0"><tr><td colspan=2 style="padding-top: 14px;"><b>Thursday July 19, 2018</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>9:00&#8211;9:15</b></td><td valign=top style="padding-top: 14px;"><b>Opening remarks</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>9:15&#8211;10:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 1: Clinical NLP</b></td></tr>
 +
<tr><td valign=top width=100>9:15&#8211;9:30</td><td valign=top align=left><i>Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility</i><br>
 +
Denis Newman-Griffis and Ayah Zirikly</td></tr>
 +
<tr><td valign=top width=100>9:30&#8211;9:45</td><td valign=top align=left><i>Multi-task learning for interpretable cause of death classification using key phrase prediction</i><br>
 +
Serena Jeblee, Mireille Gomes and Graeme Hirst</td></tr>
 +
<tr><td valign=top width=100>9:45&#8211;10:00</td><td valign=top align=left><i>Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach</i><br>
 +
Thanat Chokwijitkul, Anthony Nguyen, Hamed Hassanzadeh and Siegfried Perez</td></tr>
 +
<tr><td valign=top width=100>10:00&#8211;10:15</td><td valign=top align=left><i>Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks</i><br>
 +
Ilham Fathy Saputra, Rahmad Mahendra and Alfan Farizki Wicaksono</td></tr>
 +
<tr><td valign=top width=100>10:15&#8211;10:30</td><td valign=top align=left><i>Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning</i><br>
 +
Jurica &#352;eva, Martin Wackerbauer and Ulf Leser</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>10:30&#8211;11:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>11:00&#8211;12:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 2: Foundations</b></td></tr>
 +
<tr><td valign=top width=100>11:00&#8211;11:15</td><td valign=top align=left><i>Ontology alignment in the biomedical domain using entity definitions and context</i><br>
 +
Lucy Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm and Waleed Ammar</td></tr>
 +
<tr><td valign=top width=100>11:15&#8211;11:30</td><td valign=top align=left><i>Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization</i><br>
 +
Dieter Galea, Ivan Laponogov and Kirill Veselkov</td></tr>
 +
<tr><td valign=top width=100>11:30&#8211;11:45</td><td valign=top align=left><i>PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks</i><br>
 +
Di Jin and Peter Szolovits</td></tr>
 +
<tr><td valign=top width=100>11:45&#8211;12:00</td><td valign=top align=left><i>Coding Structures and Actions with the COSTA Scheme in Medical Conversations</i><br>
 +
Nan Wang, Yan Song and Fei Xia</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>12:00&#8211;13:30</b></td><td valign=top style="padding-top: 14px;"><b><em>Lunch break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>13:30&#8211;14:30</b></td><td valign=top style="padding-top: 14px;"><b>Invited Talk: "Automating systematic reviews: progress and challenges" &#8211; Paul Glasziou</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:30&#8211;15:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 3 Literature mining and retrieval; Question Answering</b></td></tr>
 +
<tr><td valign=top width=100>14:30&#8211;14:45</td><td valign=top align=left><i>A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval</i><br>
 +
Jonas Pfeiffer, Samuel Broscheit, Rainer Gemulla and Mathias G&ouml;schl</td></tr>
 +
<tr><td valign=top width=100>14:45&#8211;15:00</td><td valign=top align=left><i>Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing</i><br>
 +
Jari Bj&ouml;rne and Tapio Salakoski</td></tr>
 +
<tr><td valign=top width=100>15:00&#8211;15:15</td><td valign=top align=left><i>BioAMA: Towards an End to End BioMedical Question Answering System</i><br>
 +
Vasu Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg and Teruko Mitamura</td></tr>
 +
<tr><td valign=top width=100>15:15&#8211;15:30</td><td valign=top align=left><i>Phrase2VecGLM: Neural generalized language model&#8211;based semantic tagging for complex query reformulation in medical IR</i><br>
 +
Manirupa Das, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, David Chen, Steve Rust, Yungui Huang and Rajiv Ramnath</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>15:30&#8211;16:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>16:00&#8211;16:15</b></td><td valign=top style="padding-top: 14px;"><b>Invited Presentation: "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature" &#8211; Ben Nye</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>16:15&#8211;18:00</b></td><td valign=top style="padding-top: 14px;"><b>Poster Session</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings</i><br>
 +
Dat Quoc Nguyen and Karin Verspoor</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Domain Adaptation for Disease Phrase Matching with Adversarial Networks</i><br>
 +
Miaofeng Liu, Jialong Han, Haisong Zhang and Yan Song</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records</i><br>
 +
Mohamad Salimi and Alla Rozovskaya</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model</i><br>
 +
Qiuyue Wang and Xiaofeng Meng</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>SingleCite: Towards an improved Single Citation Search in PubMed</i><br>
 +
Lana Yeganova, Donald C Comeau, Won Kim, W John Wilbur and Zhiyong Lu</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity</i><br>
 +
Mengnan Zhao, Aaron J. Masino and Christopher C. Yang</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>MeSH-based dataset for measuring the relevance of text retrieval</i><br>
 +
Won Gyu KIM, Lana Yeganova, Donald comeau, W John Wilbur and Zhiyong Lu</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs</i><br>
 +
Kaiyin Zhou, Sheng Zhang, Xiangyu Meng, Qi Luo, Yuxing Wang, Ke Ding, Yukun Feng, Mo Chen, Kevin Cohen and Jingbo Xia</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Prediction Models for Risk of Type-2 Diabetes Using Health Claims</i><br>
 +
Masatoshi Nagata, Kohichi Takai, Keiji Yasuda, Panikos Heracleous and Akio Yoneyama</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data</i><br>
 +
Yaqiang Wang, Yunhui Chen, Hongping Shu and Yongguang Jiang</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts</i><br>
 +
Long Trieu, Nhung Nguyen, Makoto Miwa and Sophia Ananiadou</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Toward Cross-Domain Engagement Analysis in Medical Notes</i><br>
 +
Sara Rosenthal and Adam Faulkner</td></tr>
 +
</table>
 +
 
 +
 
 +
==SUBMISSION INSTRUCTIONS==
 +
 
 +
Two types of submissions are invited: full papers and short papers.
 +
Submissions are due by 11:59 PM EST on Monday April 16, 2018.
 +
 
 +
Full papers should not exceed eight (8) pages of text, plus unlimited references. These are intended to be reports of original research.
 +
BioNLP aims to be the forum for interesting, innovative, and promising work involving biomedicine and language technology, whether or not
 +
yielding high performance at the moment. This by no means precludes our interest in and preference for mature results, strong performance,
 +
and thorough evaluation.  Both types of research and combinations thereof are encouraged. 
 +
 
 +
Short papers may consist of up to four (4) pages of content, plus unlimited references. Upon acceptance, short papers will still be given four (4)
 +
content pages in the proceedings. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
 +
 
 +
Electronic Submission<br/>
 +
Submissions must be electronic and in PDF format, using the Softconf START conference management system at '''https://www.softconf.com/acl2018/BioNLP/'''
 +
 
 +
We strongly recommend consulting ACL new policies for submission, review, and citation:
 +
https://www.aclweb.org/portal/content/new-policies-submission-review-and-citation  and using ACL LaTeX style files tailored for this year's conference.
 +
 
 +
Submissions must conform to the official style guidelines. Style files and other information about paper formatting requirements
 +
are available on the conference website, http://acl2018.org/call-for-papers/. Scroll down to “Paper Submission and Templates.”
 +
 
 +
Submissions need to be anonymous.
 +
 
 +
Dual submission policy: papers may NOT be submitted to the BioNLP 2018 workshop if they are or will be concurrently submitted to another meeting or publication.
 +
 
 +
==KEYNOTE==
 +
 
 +
Keynote presentation by Dr. Paul Glasziou  [https://bond.edu.au/intl/profile/paul-glasziou]
 +
 
 +
 
 +
==WORKSHOP OVERVIEW AND SCOPE==
 +
 
 +
Over the course of the past sixteen years, the ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP and exposing these researchers to the mainstream ACL research, and a venue for informing the mainstream ACL researchers about the fast growing and important domain.
 +
The workshop will continue presenting work on a broad and interesting range of topics in NLP. This year we are expanding our interests, and in addition to the topics listed below, we welcome submissions on the  lessons learned while reproducing published results.
 +
 
 +
The active areas of research include, but are not limited to:
 +
* Entity identification and normalization for a broad range of semantic categories
 +
* Extraction of complex relations and events
 +
* Semantic parsing
 +
* Discourse analysis
 +
* Anaphora /Coreference resolution
 +
* Text mining
 +
* Literature based discovery
 +
* Summarization
 +
* Question Answering
 +
* Resources and novel strategies for system testing and evaluation
 +
* Infrastructures for biomedical text mining
 +
* Processing and annotation platforms
 +
* Translating NLP research to practice
 +
* Theoretical underpinnings of biomedical language processing
 +
* Research Reproducibility
 +
 
 +
====Program Committee:====
 +
 
 +
  * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
 +
  * Emilia Apostolova, Language.ai, USA
 +
  * Eiji Aramaki, University of Tokyo, Japan
 +
  * Asma Ben Abacha, US National Library of Medicine
 +
  * Olivier Bodenreider, US National Library of Medicine
 +
  * Leonardo Campillos Llanos, LIMSI - CNRS, France
 +
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA
 +
  * Brian Connolly, Kroger Digital, USA
 +
  * Dina Demner-Fushman, US National Library of Medicine
 +
  * Filip Ginter, University of Turku, Finland
 +
  * Cyril Grouin, LIMSI - CNRS, France
 +
  * Tudor Groza, The Garvan Institute of Medical Research, Australia
 +
  * Graciela Gonzalez, University of Pennsylvania, USA
 +
  * Travis Goodwin, The University of Texas at Dallas, USA
 +
  * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 +
  * Halil Kilicoglu, US National Library of Medicine
 +
  * Robert Leaman, US National Library of Medicine
 +
  * Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany
 +
  * Zhiyong Lu, US National Library of Medicine
 +
  * Timothy Miller, Children’s Hospital Boston, USA
 +
  * Makoto Miwa, Toyota Technological Institute, Japan
 +
  * Danielle L Mowery, VA Salt Lake City Health Care System, USA
 +
  * Yassine M'Rabet, US National Library of Medicine
 +
  * Aurelie Neveol, LIMSI - CNRS, France
 +
  * Claire Nédellec, INRA, France
 +
  * Mariana Neves, Hasso Plattner Institute and University of Potsdam, Germany
 +
  * Nhung Nguyen, The University of Manchester, UK
 +
  * Naoaki Okazaki, Tohoku University, Japan
 +
  * Sampo Pyysalo, University of Cambridge, UK
 +
  * Francisco J. Ribadas-Pena, University of Vigo, Spain
 +
  * Fabio Rinaldi,  University of Zurich, Switzerland
 +
  * Kirk Roberts, The University of Texas Health Science Center at Houston, USA
 +
  * Angus Roberts, The University of Sheffield, UK
 +
  * Hagit Shatkay, University of Delaware, USA
 +
  * Pontus Stenetorp, University College London, UK
 +
  * Karin Verspoor, The University of Melbourne, Australia
 +
  * Byron C. Wallace,  University of Texas at Austin, USA
 +
  * Jingbo Xia, Huazhong Agricultural University, China
 +
  * Pierre Zweigenbaum, LIMSI - CNRS, France
 +
 
 +
<!-- An ACL 2017 Workshop associated with the SIGBIOMED special interest group, featuring an associated task: BioASQ ( http://www.bioasq.org/)
 +
 
 +
Vancouver, Canada, Friday August 4, 2017
 +
 
 +
 
 +
===IMPORTANT DATES===
 +
 
 +
*Submission deadline: Wednesday April 26, 2017 11:59 PM Eastern US
 +
*Notification of acceptance: Monday May 22, 2017
 +
*'Camera-ready copy due from authors:  Friday May 26, 2017
 +
*'''Workshop: Friday August 4, 2017'''
 +
-->
 +
<!--
 +
==BIONLP 2017 Workshop Schedule==
 +
 
 +
<table cellspacing="0" cellpadding="5" border="0"><tr><td colspan=2 style="padding-top: 14px;"><b>Friday August 4, 2017</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>8:30&#8211;8:45</b></td><td valign=top style="padding-top: 14px;"><b>Opening remarks</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>8:45&#8211;10:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 1: Prediction and relation extraction</b></td></tr>
 +
<tr><td valign=top width=100>8:45&#8211;9:00</td><td valign=top align=left><i>Target word prediction and paraphasia classification in spoken discourse
 +
</i><li>Joel Adams, Steven Bedrick, Gerasimos Fergadiotis, Kyle Gorman and Jan van Santen </li>
 +
</td></tr>
 +
<tr><td valign=top width=100>9:00&#8211;9:15</td><td valign=top align=left><i>Extracting Drug-Drug Interactions with Attention CNNs
 +
</i><li>Masaki Asada, Makoto Miwa, Yutaka Sasaki</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>9:15&#8211;9:30</td><td valign=top align=left><i>Insights into Analogy Completion from the Biomedical Domain
 +
</i><li>Denis Newman-Griffis, Albert Lai, Eric Fosler-Lussier</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>9:30&#8211;9:45</td><td valign=top align=left><i>Deep learning for extracting protein-protein interactions from biomedical literature
 +
</i><li>Yifan Peng and Zhiyong Lu </li>
 +
</td></tr>
 +
<tr><td valign=top width=100>9:45&#8211;10:00</td><td valign=top align=left><i>Stacking With Auxiliary Features for Entity Linking in the Medical Domain
 +
</i><li>Nazneen Fatema Rajani, Mihaela Bornea, Ken Barker</li>
 +
</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>10:00&#8211;10:30</b></td><td valign=top style="padding-top: 14px;"><b>Invited Talk: <i>Results of the 5th edition of BioASQ Challenge</i> <br> Speaker:  Georgios Paliouras</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>10:30&#8211;11:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>11:00&#8211;12:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 2: BioASQ 2017 and more</b></td></tr>
 +
<tr><td valign=top width=100>11:00&#8211;11:15</td><td valign=top align=left><i>Tackling Biomedical Text Summarization: OAQA at BioASQ 5B
 +
</i><li>
 +
Khyathi Chandu, Aakanksha Naik, Aditya Chandrasekar, Zi Yang, Niloy Gupta, Eric Nyberg</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>11:15&#8211;11:30</td><td valign=top align=left><i>Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers
 +
</i><li>Diego Molla</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>11:30&#8211;11:45</td><td valign=top align=left><i>Neural Question Answering at BioASQ 5B
 +
</i><li>Georg Wiese, Dirk Weissenborn, Mariana Neves</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>11:45&#8211;12:00</td><td valign=top align=left><i>End-to-End System for Bacteria Habitat Extraction </i>
 +
<li>Farrokh Mehryary, Kai Hakala, Suwisa Kaewphan, Jari Björne, Tapio Salakoski, Filip Ginter</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>12:00&#8211;12:15</td><td valign=top align=left><i>Creation and evaluation of a dictionary-based tagger for virus species and proteins</i><li>
 +
Helen Cook, Rudolfs Berzins, Cristina Leal Rodrıguez, Juan Miguel Cejuela, Lars Juhl Jensen</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>12:15&#8211;12:30</td><td valign=top align=left><i>Representation of complex terms in a vector space structured by an ontology for a normalization task</i><li>
 +
Arnaud Ferré, Pierre Zweigenbaum, Claire Nédellec</li>
 +
</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>12:30&#8211;14:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Lunch break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:00&#8211;15:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 3: From bio to clinical NLP</b></td></tr>
 +
<tr><td valign=top width=100>14:00&#8211;14:15</td><td valign=top align=left><i>Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors
 +
</i><li>Bridget McInnes and Ted Pedersen</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>14:15&#8211;14:30</td><td valign=top align=left><i>Proactive Learning for Named Entity Recognition
 +
</i><li>Maolin Li, Nhung Nguyen, Sophia Ananiadou</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>14:30&#8211;14:45</td><td valign=top align=left><i>Biomedical Event Extraction using Abstract Meaning Representation
 +
</i><li>Sudha Rao, Daniel Marcu, Kevin Knight, Hal Daumé III</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>14:45&#8211;15:00</td><td valign=top align=left><i>Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System
 +
</i><li>Ari Klein, Abeed Sarker, Masoud Rouhizadeh, Karen O'Connor, Graciela Gonzalez</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>15:00&#8211;15:15</td><td valign=top align=left><i>Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings
 +
</i><li>Pieter Fivez, Simon Suster, Walter Daelemans</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>15:15&#8211;15:30</td><td valign=top align=left><i>Characterization of Divergence in Impaired Speech of ALS Patients
 +
</i><li>
 +
Archna Bhatia, Bonnie Dorr, Kristy Hollingshead, Samuel L. Phillips, Barbara McKenzie</li>
 +
</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>15:30&#8211;16:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>16:00&#8211;16:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 3: More clinical NLP</b></td></tr>
 +
<tr><td valign=top width=100>16:00&#8211;16:15</td><td valign=top align=left><i>Deep Learning for Punctuation Restoration in Medical Reports
 +
</i><li>Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>16:15&#8211;16:30</td><td valign=top align=left><i>Unsupervised Domain Adaptation for Clinical Negation Detection
 +
</i><li>Timothy Miller, Steven Bethard, Hadi Amiri, Guergana Savova</li>
 +
</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>16:30&#8211;18:00</b></td><td valign=top style="padding-top: 14px;"><b>Poster Session</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations</i><li>
 +
Rezarta Islamaj Dogan, Andrew Chatr-aryamontri, Sun Kim, Chih-Hsuan Wei, Yifan Peng, Donald Comeau, Zhiyong Lu</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Painless Relation Extraction with Kindred
 +
</i><li>Jake Lever and Steven Jones</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction</i><li>Gang Li, Cathy Wu, K. Vijay-Shanker</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing </i><li>
 +
Seth Polsley, Atif Tahir, Muppala Raju, Akintayo Akinleye, Duane Steward</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Annotation of pain and anesthesia events for surgery-related processes and outcomes extraction</i><li>
 +
Wen-wai Yim, Dario Tedesco, Catherine Curtin, Tina Hernandez-Boussard</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Identifying Comparative Structures in Biomedical Text
 +
</i><li>Samir Gupta, A.S.M. Ashique Mahmood, Karen Ross, Cathy Wu, K. Vijay-Shanker</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models</i><li>
 +
Subhradeep Kayal, Zubair Afzal, George Tsatsaronis, Sophia Katrenko, Pascal Coupet, Marius Doornenbal, Michelle Gregory</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs </i><li>
 +
Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia</i><li>
 +
Vaden Masrani, Gabriel Murray, Thalia Field, Giuseppe Carenini</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Protein Word Detection using Text Segmentation Techniques</i><li>
 +
Devi Ganesan, Ashish V. Tendulkar, Sutanu Chakraborti</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway</i><li>
 +
Wojciech Kusa and Michael Spranger</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes</i><li>
 +
Emilia Apostolova and Tom Velez</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Enhancing Automatic ICD-9-CM Code Assignment for \\Medical Texts with PubMed
 +
</i><li>Danchen Zhang, Daqing He, Sanqiang Zhao, Lei Li</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation
 +
</i><li>Sam Henry, Clint Cuffy, Bridget McInnes</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study
 +
</i><li>Danielle Mowery,  Brett South, Olga Patterson, Shu-Hong Zhu, Mike Conway</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i> Automated Preamble Detection in Dictated Medical Reports
 +
</i><li>Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>A Biomedical Question Answering System in BioASQ 2017
 +
</i><li>Mourad Sarrouti and Said Ouatik El Alaoui</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Adapting Pre-trained Word Embeddings For Use In Medical Coding
 +
</i><li>Kevin Patel,  Divya Patel, Mansi Golakiya, Pushpak Bhattacharyya, Nilesh Birari</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Initializing neural networks for hierarchical multi-label text classification
 +
</i><li>Simon Baker and Anna Korhonen</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
 +
</i><li>Rahul V S S Patchigolla, Sunil Sahu, Ashish Anand</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
 +
</i><li>Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
 +
</i><li>Sarvnaz Karimi, Xiang Dai, Hamedh Hassanzadeh, Anthony Nguyen</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Automatic classification of doctor-patient questions for a virtual patient record query task.
 +
</i><li>Leonardo Campillos Llanos, Sophie Rosset, Pierre Zweigenbaum</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Assessing the performance of Olelo, a real-time biomedical question answering application
 +
</i><li>Mariana Neves, Fabian Eckert, Hendrik Folkerts, Matthias Uflacker</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Clinical Event Detection with Hybrid Neural Architecture
 +
</i><li>Adyasha Maharana</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision
 +
</i><li>Aakanksha Naik, Chris Bogart, Carolyn Rose</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Detecting mentions of pain and acute confusion in Finnish clinical text
 +
</i><li>Hans Moen, Kai Hakala, Farrokh Mehryary, Laura-Maria Peltonen, Tapio Salakoski, Filip Ginter, Sanna Salanterä</li>
 +
</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>A Multi-strategy Query Processing Approach for Biomedical Question Answering: USTB\_PRIR at BioASQ 2017 Task 5B
 +
</i><li>Zan-Xia Jin, Bo-Wen Zhang, Fan Fang, Le-Le Zhang, Xu-Cheng Yin</li>
 +
</td></tr>
 +
</table>
 +
-->
 +
<!--
 +
 
 +
===SUBMISSION INSTRUCTIONS===
 +
 
 +
Two types of submissions are invited: full papers and short papers.
 +
Submissions are due by 11:59 PM EST on Wednesday April 26, 2017.
 +
 
 +
Full papers should not exceed eight (8) pages of text, plus two pages of references. These are intended to be reports of original research. BioNLP aims to be the forum for interesting, innovative, and promising work involving biomedicine and language technology, whether or not yielding high performance at the moment. This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation.  Both types of research and combinations thereof are encouraged. 
 +
 
 +
Short papers may consist of up to four (4) pages of content, plus unlimited references. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
 +
 
 +
Electronic Submission
 +
Submissions must be electronic and in PDF format, using the Softconf START conference management system at https://www.softconf.com/acl2017/bionlp/
 +
We strongly recommend the use of ACL LaTeX style files tailored for this year's conference. Submissions must conform to the official style guidelines. Style files and other information about paper formatting requirements are available on the conference website, http://acl2017.org/. 
 +
 
 +
Submissions need to be anonymous.
 +
 
 +
Dual submission policy: papers may NOT be submitted to  the BioNLP 2017 workshop if they are or will be concurrently submitted to another meeting or publication.
 +
 
 +
-->
 +
<!--
 +
===WORKSHOP OVERVIEW AND SCOPE===
 +
 
 +
Over the course of the past fifteen years, the ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP and exposing these researchers to the mainstream ACL research, and a venue for informing the mainstream ACL researchers about the fast growing and important domain.
 +
The workshop will continue presenting work on a broad and interesting range of topics in NLP.
 +
 
 +
The active areas of research include, but are not limited to:
 +
* Entity identification and normalization for a broad range of semantic categories
 +
* Extraction of complex relations and events
 +
* Semantic parsing
 +
* Discourse analysis
 +
* Anaphora /Coreference resolution
 +
* Text mining
 +
* Literature based discovery
 +
* Summarization
 +
* Question Answering
 +
* Resources and novel strategies for system testing and evaluation
 +
* Infrastructures for biomedical text mining
 +
* Processing and annotation platforms
 +
* Translating NLP research to practice
 +
* Theoretical underpinnings of biomedical language processing
 +
* Research Reproducibility
 +
-->
 +
<!--
 +
====Program Committee:====
 +
 
 +
  * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
 +
  * Ion Androutsopoulos, Athens University of Economics and Business, Greece
 +
  * Emilia Apostolova, Language.ai, USA
 +
  * Eiji Aramaki, University of Tokyo, Japan
 +
  * Alan Aronson, US National Library of Medicine
 +
  * Asma Ben Abacha, US National Library of Medicine
 +
  * Olivier Bodenreider, US National Library of Medicine
 +
  * Leonardo Campillos Llanos, LIMSI - CNRS, France
 +
  * Juan Miguel Cejuela, Technische Universit&auml;t M&uuml;nchen, Germany
 +
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA
 +
  * Nigel Collier, University of Cambridge, UK
 +
* Aaron Cohen, Oregon Health and Science University
 +
  * Dina Demner-Fushman, US National Library of Medicine
 +
* Patrick Gallinari, Université Pierre et Marie Curie, France
 +
  * Filip Ginter, University of Turku, Finland
 +
  * Graciela Gonzalez, University of Pennsylvania, USA
 +
  * Cyril Grouin, LIMSI - CNRS, France
 +
  * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 +
  * Aris Kosmopoulos, NCSR Demokritos, Greece
 +
  * Halil Kilicoglu, US National Library of Medicine
 +
  * Robert Leaman, US National Library of Medicine
 +
* Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany
 +
  * Chris Lu, US National Library of Medicine
 +
  * Zhiyong Lu, US National Library of Medicine
 +
  * Prodromes Malakasiotis, Athens University of Economics and Business, Greece
 +
  * Timothy Miller, Children’s Hospital Boston, USA
 +
  * Makoto Miwa, Toyota Technological Institute, Japan
 +
  * Diego Molla, Macquarie University, Australia
 +
  * Jim Mork, National Library of Medicine, USA
 +
  * Danielle L Mowery, VA Salt Lake City Health Care System, USA
 +
  * Yassine M'Rabet, US National Library of Medicine
 +
  * Henning M&uuml;ller, University of Applied Sciences, Switzerland
 +
  * Claire Nédellec, INRA, France
 +
  * Anastasios Nentidis, NCSR Demokritos, Athens, Greece
 +
  * Aurelie Neveol, LIMSI - CNRS, France
 +
  * Mariana Neves, Hasso Plattner Institute and University of Potsdam, Germany
 +
  * Nhung Nguyen, The University of Manchester, UK
 +
  * Naoaki Okazaki, Tohoku University, Japan
 +
  * Georgios Paliouras, NCSR Demokritos, Athens, Greece
 +
  * Ioannis Partalas, Viseo group, France
 +
  * John Prager, Thomas J. Watson Research Center, IBM, USA
 +
  * Sampo Pyysalo, University of Cambridge, UK
 +
* Bastien Rance, Hopital Europeen Georges Pompidou, France
 +
  * Francisco J. Ribadas-Pena, University of Vigo, Spain
 +
  * Fabio Rinaldi,  University of Zurich, Switzerland
 +
  * Kirk Roberts, The University of Texas Health Science Center at Houston, USA
 +
  * Angus Roberts, The University of Sheffield, UK
 +
* Yoshimasa Tsuruoka, University of Tokyo, Japan
 +
  * Hagit Shatkay, University of Delaware, USA
 +
  * Pontus Stenetorp, University College London, UK
 +
* George Tsatsaronis, Technical University of Dresden, Germany
 +
  * Karin Verspoor, The University of Melbourne, Australia
 +
  * Ellen Voorhees, National Institute of Standards and Technology, USA
 +
  * Byron C. Wallace,  University of Texas at Austin, USA
 +
  * W John Wilbur, US National Library of Medicine
 +
  * Hai Zhao, Shanghai Jiao Tong University, Shanghai
 +
  * Pierre Zweigenbaum, LIMSI - CNRS, France
 +
-->
 +
<!--
 +
====Additional Reviewers:====
 +
 
 +
  * Moumita Bhattacharya, University of Delaware, USA
 +
  * Louise Deleger, INRA - MaIAGE, France
 +
  * Lenz Furrer, Institute of Computational Linguistics, UZH, Zurich, Switzerland
 +
  * Genevieve Gorrell, Sheffield University, UK
 +
  * Ari Klein, University of Pennsylvania School of Medicine
 +
  * Yifan Peng, US National Library of Medicine
 +
  * Vassiliki Rentoumi, National Centre for Scientific Research Demokritos, Athens, Greece
 +
  * Masoud Rouhizadeh, University of Pennsylvania, USA
 +
  * Abeed Sarker, University of Pennsylvania, USA
 +
  * Xingyi Song, University of Sheffield, UK
 +
  * Tasnia Tahsin, Arizona State University, USA
 +
  * Hegler Tissot, Federal University of Parana, Brazil
 +
  * Ken Yano, Nara Institute of Science and Technology, Japan
 +
-->
 +
 
 +
 
 +
 
 +
<!--
 +
===VISA information===
 +
ACL organizers provide letters needed for visas. Please scroll to the bottom of this page:
 +
http://acl2017.org/participants/ and click "Visa Letter Request Form."
 +
 
 +
 
 +
===ACL Anti-Harassment Policy===
 +
https://www.aclweb.org/adminwiki/index.php?title=Anti-Harassment_Policy
 +
 
 +
If you have any concerns  or experience harassment or hostile behavior please contact Dina Demner-Fushman,  Kevin Bretonnel Cohen or any current member of the ACL Executive Committee.
 +
-->
 +
<!--
 +
==BIONLP 2016 ==
 +
 
 +
An ACL 2016 Workshop associated with the [[SIGBIOMED]] special interest group Featuring two associated tasks: [http://www.bioasq.org/workshop/  BioASQ] (http://www.bioasq.org/workshop) and [http://2016.bionlp-st.org/  BioNLP-ST]  (http://2016.bionlp-st.org).
 +
 
 +
Berlin, Germany, August 12 -13, 2016
 +
 
 +
===IMPORTANT DATES===
 +
 
 +
* Submission deadline: Sunday, May 8, 2016, 11:59 PM Eastern US
 +
* Notification of acceptance: Monday, June 13, 2016
 +
* Camera-ready copy due from authors:  Wednesday, June 22, 2016
 +
* BioNLP workshop: Friday, August 12, 2016
 +
* BioNLP-ST and BioASQ workshop: Saturday August 13, 2016
 +
 
 +
 
 +
==BIONLP 2016 Workshop Schedule==
 +
 
 +
<table cellspacing="0" cellpadding="5" border="0"><tr><td colspan=2 style="padding-top: 14px;"><b>Friday August 12, 2016</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>8:30&#8211;8:40</b></td><td valign=top style="padding-top: 14px;"><b>Opening remarks</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>8:40&#8211;10:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 1: Entity extraction and representation</b></td></tr>
 +
<tr><td valign=top width=100>8:40&#8211;9:00</td><td valign=top align=left><i>A Machine Learning Approach to Clinical Terms Normalization</i><br>
 +
Jose Castano, Mar&iacute;a Laura Gambarte, Hee Joon Park, Maria del Pilar Avila Williams, David Perez, Fernando Campos, Daniel Luna, Sonia Benitez, Hernan Berinsky and Sof&iacute;a Zanetti</td></tr>
 +
<tr><td valign=top width=100>9:00&#8211;9:20</td><td valign=top align=left><i>Improved Semantic Representation for Domain-Specific Entities</i><br>
 +
Mohammad Taher Pilehvar and Nigel Collier</td></tr>
 +
<tr><td valign=top width=100>9:20&#8211;9:40</td><td valign=top align=left><i>Identification, characterization, and grounding of gradable terms in clinical text</i><br>
 +
Chaitanya Shivade, Marie-Catherine de Marneffe, Eric Fosler-Lussier and Albert M. Lai</td></tr>
 +
<tr><td valign=top width=100>9:40&#8211;10:00</td><td valign=top align=left><i>Graph-based Semi-supervised Gene Mention Tagging</i><br>
 +
Golnar Sheikhshab, Elizabeth Starks, Aly Karsan, Anoop Sarkar and Inanc Birol</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>10:00&#8211;10:30</b></td><td valign=top style="padding-top: 14px;"><b>Invited Talk: <i>The BioNLP-ST challenges on information extraction and knowledge acquisition in biology</i> <br> Speakers:  Robert Bossy and Jin-Dong Kim</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>10:30&#8211;11:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>11:00&#8211;12:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 2: Event and Relation Extraction</b></td></tr>
 +
<tr><td valign=top width=100>11:00&#8211;11:20</td><td valign=top align=left><i>Feature Derivation for Exploitation of Distant Annotation via Pattern Induction against Dependency Parses</i><br>
 +
Dayne Freitag and John Niekrasz</td></tr>
 +
<tr><td valign=top width=100>11:40&#8211;12:00</td><td valign=top align=left><i>Inferring Implicit Causal Relationships in Biomedical Literature</i><br>
 +
Halil Kilicoglu</td></tr>
 +
<tr><td valign=top width=100>12:00&#8211;12:20</td><td valign=top align=left><i>SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction</i><br>
 +
Marco A. Valenzuela-Esc&aacute;rcega, Gus Hahn-Powell, Dane Bell and Mihai Surdeanu</td></tr>
 +
<tr><td valign=top width=100>12:20&#8211;12:40</td><td valign=top align=left><i>Character based String Kernels for Bio-Entity Relation Detection</i><br>
 +
Ritambhara Singh and Yanjun Qi</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>12:40&#8211;14:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Lunch break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>14:00&#8211;15:40</b></td><td valign=top style="padding-top: 14px;"><b>Session 3: Disambiguation, Classification, and more</b></td></tr>
 +
<tr><td valign=top width=100>14:00&#8211;14:20</td><td valign=top align=left><i>Disambiguation of entities in MEDLINE abstracts by combining MeSH terms with knowledge</i><br>
 +
Amy Siu, Patrick Ernst and Gerhard Weikum</td></tr>
 +
<tr><td valign=top width=100>14:20&#8211;14:40</td><td valign=top align=left><i>Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts</i><br>
 +
Stephan Tulkens, Simon Suster and Walter Daelemans</td></tr>
 +
<tr><td valign=top width=100>14:40&#8211;15:00</td><td valign=top align=left><i>Unsupervised Document Classification with Informed Topic Models</i><br>
 +
Timothy Miller, Dmitriy Dligach and Guergana Savova</td></tr>
 +
<tr><td valign=top width=100>15:00&#8211;15:20</td><td valign=top align=left><i>Vocabulary Development To Support Information Extraction of Substance Abuse from Psychiatry Notes</i><br>
 +
Sumithra Velupillai, Danielle L Mowery, Mike Conway, John Hurdle and Brent Kious</td></tr>
 +
<tr><td valign=top width=100>15:20&#8211;15:40</td><td valign=top align=left><i>Syntactic analyses and named entity recognition for PubMed and PubMed Central __ up-to-the-minute</i><br>
 +
Kai Hakala, Suwisa Kaewphan, Tapio Salakoski and Filip Ginter</td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>15:40&#8211;16:00</b></td><td valign=top style="padding-top: 14px;"><b><em>Coffee Break</em></b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>16:00&#8211;16:30</b></td><td valign=top style="padding-top: 14px;"><b>Invited Talk: <i> BioASQ: A challenge on large-scale biomedical semantic indexing and question answering</i> <br> Speaker: Anastasia Krithara</b></td></tr>
 +
<tr><td valign=top style="padding-top: 14px;"><b>16:30&#8211;17:30</b></td><td valign=top style="padding-top: 14px;"><b>Poster Session</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Improving Temporal Relation Extraction with Training Instance Augmentation</i><br>
 +
Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard and Guergana Savova</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Using Centroids of Word Embeddings and Word Mover&rsquo;s Distance for Biomedical Document Retrieval in Question Answering</i><br>
 +
Georgios-Ioannis Brokos, Prodromos Malakasiotis and Ion Androutsopoulos</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway</i><br>
 +
Michael Spranger, Sucheendra Palaniappan and Samik Gosh</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Construction of a Personal Experience Tweet Corpus for Health Surveillance</i><br>
 +
Keyuan Jiang, Ricardo Calix and Matrika Gupta</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification</i><br>
 +
Nut Limsopatham and Nigel Collier</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>PubTermVariants: biomedical term variants and their use for PubMed search</i><br>
 +
Lana Yeganova, Won Kim, Sun Kim, Rezarta Islamaj Do&#287;an, Wanli Liu, Donald C Comeau, Zhiyong Lu and W John Wilbur</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>This before That: Causal Precedence in the Biomedical Domain</i><br>
 +
Gus Hahn-Powell, Dane Bell, Marco A. Valenzuela-Esc&aacute;rcega and Mihai Surdeanu</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Syntactic methods for negation detection in radiology reports in Spanish</i><br>
 +
Viviana Cotik, Vanesa Stricker, Jorge Vivaldi and Horacio Rodriguez</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>How to Train good Word Embeddings for Biomedical NLP</i><br>
 +
Billy Chiu, Gamal Crichton, Anna Korhonen and Sampo Pyysalo</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>An Information Foraging Approach to Determining the Number of Relevant Features</i><br>
 +
Brian Connolly, Benjamin Glass and John Pestian</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Assessing the Feasibility of an Automated Suggestion System for Communicating Critical Findings from Chest Radiology Reports to Referring Physicians</i><br>
 +
Brian E. Chapman, Danielle L Mowery, Evan Narasimhan, Neel Patel, Wendy Chapman and Marta Heilbrun</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Building a dictionary of lexical variants for phenotype descriptors</i><br>
 +
Simon Kocbek and Tudor Groza</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections</i><br>
 +
Olof Jacobson and Hercules Dalianis</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning</i><br>
 +
Genevieve Gorrell, Sherifat Oduola, Angus Roberts, Tom Craig, Craig Morgan and Rob Stewart</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Relation extraction from clinical texts using domain invariant convolutional neural network</i><br>
 +
Sunil Sahu, Ashish Anand, Krishnadev Oruganty and Mahanandeeshwar Gattu</td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Results of the 4th edition of BioASQ Challenge</i><br>Anastasia Krithara, Anastasios Nentidis, Georgios Paliouras and Ioannis Kakadiaris</td></tr>
 +
 
 +
<tr><td valign=top width=100>&nbsp;</td> <td><b>All  August 13 talks are also presented as posters on August 12 and August 13</b></td></tr>
 +
 
 +
</table>
 +
 
 +
==BioASQ / BioNLP-ST Workshop Program, August 13==
 +
 
 +
<table>
 +
      <tr><td valign=top width=100>'''9:00-9:15'''</td> <td><b>Welcome</b></td></tr>
 +
      <tr><td valign=top width=100>'''9:15-10:15'''</td> <td>'''Invited speaker: Sherri Matis-Mitchell''' <br>Solving Problems and Supporting Decisions in Pharma R&D using Text Analytics: A Recent History</td></tr>
 +
      <tr><td valign=top width=100>10:15-10:30</td> <td>'''Overview of BioASQ'''</td></tr>
 +
      <tr><td valign=top width=100>10:30-11:00</td> <td><b>Coffee break</b></td></tr>
 +
      <tr><td valign=top width=100>11:00-12:30</td> <td><b>BioASQ participant session</b></td></tr>
 +
      <tr><td valign=top width=100>11:00-11:15</td> <td>Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results<br>Ilya Zavorin, James Mork and Dina Demner-Fushman</td></tr>
 +
      <tr><td valign=top width=100>11:15-11:30</td> <td>LABDA at the 2016 BioASQ challenge task 4a: Semantic Indexing by using ElasticSearch <br>Isabel Segura-Bedmar, Adrián Carruana and Paloma Martínez</td></tr>
 +
      <tr><td valign=top width=100>11:30-11:45</td> <td>Learning to Answer Biomedical Questions: OAQA at BioASQ 4B<br>Zi Yang, Yue Zhou and Eric Nyberg</td></tr>
 +
        <tr><td valign=top width=100>11:45-12:00</td> <td>HPI Question Answering System in BioASQ 2016 <br>Frederik Schulze, Ricarda Schuler, Tim Draeger, Daniel Dummer, Alexander Ernst, Pedro Flemming, Cindy Perscheid, Mariana Neves</td></tr>
 +
      <tr><td valign=top width=100>12:00-12:15</td> <td>KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge<br>Hyeon-gu Lee, Minkyoung Kim, Harksoo Kim, Juae Kim, Sunjae Kwon, Jungyun Seo, Yi-Reun Kim and Jung-Kyu Choi</td></tr>
 +
  <tr><td valign=top width=100>12:15-12:30</td> <td>Large-Scale Semantic Indexing and Question Answering in Biomedicine <br>Eirini Papagiannopoulou, Yiannis Papanikolaou, Dimitris Dimitriadis, Sakis Lagopoulos, Grigorios Tsoumakas, Manos Laliotis, Nikos Markantonatos and Ioannis Vlahavas</td></tr>
 +
   
 +
<tr><td valign=top width=100>'''12:30-14:00'''</td> <td><b>Lunch break</b></td></tr>
 +
     
 +
      <tr><td valign=top width=100>'''14:00-14:15'''</td> <td><b>Overview of BioNLP-ST</b></td></tr>
 +
     
 +
      <tr><td valign=top width=100>'''14:15-15:30'''</td> <td>'''BioNLP-ST participant session 1'''</td></tr>
 +
     
 +
      <tr><td valign=top width=100>14:15-14:30</td> <td>LitWay, discriminative extraction for different bio-events<br>Chen Li, Zhiqiang Rao and Xiangrong Zhang</td></tr>
 +
      <tr><td valign=top width=100>14:30-14:45</td> <td>VERSE: Event and relation extraction in the BioNLP 2016 Shared Task<br>Jake Lever and Steven JM Jones</td></tr>
 +
      <tr><td valign=top width=100>14:45-15:00</td> <td>A dictionary- and rule-based system for identification of bacteria and habitats in text<br>Helen Cook, Evangelos Pafilis and Lars Juhl Jensen</td></tr>
 +
    <tr><td valign=top width=100>15:00-15:15</td> <td>Extraction of Regulatory Events Using Kernel-based Classifiers and Distant Supervision<br>Andre Lamurias, Miguel J. Rodrigues, Luka A. Clarke and Francisco M Couto</td></tr>
 +
      <tr><td valign=top width=100>15:15-15:30</td>  <td>Deep Learning With Minimal Training Data: TurkuNLP Entry in The BioNLP Shared Task 2016<br>Farrokh Mehryary, Jari Björne, Sampo Pyysalo, Tapio Salakoski and Filip Ginter</td></tr>
 +
    <tr><td valign=top width=100>'''15:30-16:00'''</td> <td>'''Coffee break'''</td></tr>
 +
      <tr><td valign=top width=100>16:00-17:00</td> <td>'''BioNLP-ST participant session 2'''</td></tr>
 +
      <tr><td valign=top width=100>16:00-16:15</td><td>Identification of mentions and relations between bacteria and biotope from PubMed abstracts<br>Cyril Grouin</td></tr>
 +
  <tr><td valign=top width=100>16:15-16:30</td> <td>SeeDev Binary Event Extraction Using SVMs and a Rich Feature set<br>Nagesh Panyam Chandrasekarasastry, Gitansh Khirbat, Karin Verspoor, Trevor Cohn and Kotagiri Ramamohanarao</td></tr>
 +
      <tr><td valign=top width=100>16:30-16:45</td> <td>Ontology Based Categorization of Bacteria and Habitat Entities using Information Retrieval Techniques<br>Mert Tiftikci, Hakan Şahin, Berfu Büyüköz, Alper Yayıkçı and Arzucan Özgür</td></tr>
 +
      <tr><td valign=top width=100>16:45-17:00</td> <td>DUTIR in BioNLP-ST 2016: Utilizing convolutional network and distributed representation to extract complicate relations<br>Honglei Li, Jianhai Zhang, Jian Wang, Hongfei Lin and Zhihao Yang</td></tr>
 +
     
 +
      <tr><td valign=top width=100>'''17:00-17:30'''</td> <td><b>Closing session</b></td></tr>
 +
 
 +
</table>
 +
<!--
 +
==WORKSHOP OVERVIEW AND SCOPE==
 +
 
 +
Over the course of the past fourteen years, the ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP and exposing these researchers to the mainstream ACL research, and a venue for informing the mainstream ACL researchers about the fast growing and important domain. 
 +
 +
The workshop will continue presenting work on a broad and interesting range of topics in NLP. 
 +
 
 +
The active areas of research include:
 +
 
 +
* Entity identification and normalization for a broad range of semantic categories
 +
* Extraction of complex relations and events
 +
* Semantic parsing
 +
* Discourse analysis
 +
* Anaphora /Coreference resolution
 +
* Text mining
 +
** Literature based discovery
 +
* Summarization
 +
* Question Answering
 +
* Resources and novel strategies for system testing and evaluation
 +
** Infrastructures for biomedical text mining
 +
* Processing and annotation platforms
 +
* Translating NLP research to practice
 +
* Theoretical underpinnings of biomedical language processing
 +
-->
 +
 
 +
 
 +
 
 +
<!-- <h5>SUBMISSION INSTRUCTIONS</h5>
 +
We invite two types of submissions: full papers and short papers.
 +
Submissions are due by 11:59 PM EST on Sunday May 8, 2016.
 +
 
 +
Full papers should not exceed eight (8) pages of text, plus unlimited references. These are intended to be
 +
reports of original research. BioNLP aims to be the forum for interesting, innovative, and promising work
 +
involving biomedicine and language technology, whether or not yielding high performance at the moment.
 +
This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation.  Both types of research and combinations thereof are encouraged. 
 +
 
 +
Short papers may consist of up to four (4) pages of content, plus unlimited references.
 +
Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
 +
 
 +
<h6>Electronic Submission</h6>
 +
Submissions must be electronic and in PDF format, using the Softconf START conference management system at [https://www.softconf.com/acl2016/BioNLP16/ https://www.softconf.com/acl2016/BioNLP16/]
 +
We strongly recommend the use of ACL LaTeX style files tailored for this year's conference. Submissions must conform to the official style guidelines. Style files and other information about paper formatting requirements will be made available on the conference website,[http://acl2016.org http://acl2016.org].
 +
 
 +
<b>Submissions should be anonymous. </b>
 +
Dual submission policy: papers may <b>NOT</b> be submitted to  the BioNLP 2016 workshop if they are or will be concurrently submitted to another meeting or publication.
 +
 +
<!--
 +
<b>Program Committee:</b>
 +
 
 +
  * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
 +
  * Eiji Aramaki, University of Tokyo, Japan
 +
  * Alan Aronson, US National Library of Medicine
 +
  * Asma Ben Abacha, US National Library of Medicine
 +
  * Olivier Bodenreider, US National Library of Medicine
 +
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA
 +
  * Aaron Cohen, Oregon Health and Science University
 +
  * Dina Demner-Fushman, US National Library of Medicine
 +
  * Filip Ginter, University of Turku, Finland
 +
  * Cyril Grouin, LIMSI - CNRS, France
 +
  * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 +
  * Halil Kilicoglu, US National Library of Medicine
 +
  * Robert Leaman, US National Library of Medicine
 +
* Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany
 +
  * Zhiyong Lu, US National Library of Medicine
 +
  * Timothy Miller, Children’s Hospital Boston, USA
 +
  * Makoto Miwa, Toyota Technological Institute, Japan
 +
  * Danielle L Mowery, VA Salt Lake City Health Care System, USA
 +
  * Yassine M'Rabet, US National Library of Medicine
 +
  * Aurelie Neveol, LIMSI - CNRS, France
 +
  * Nhung Nguyen, The University of Manchester, UK
 +
  * Naoaki Okazaki, Tohoku University, Japan
 +
  * Sampo Pyysalo, University of Cambridge, UK
 +
  * Bastien Rance, Hopital Europeen Georges Pompidou, France
 +
  * Fabio Rinaldi,  University of Zurich, Switzerland
 +
  * Thomas Rindflescht, US National Library of Medicine
 +
  * Kirk Roberts, The University of Texas Health Science Center at Houston, USA
 +
  * Angus Roberts, The University of Sheffield, UK
 +
  * Yoshimasa Tsuruoka, University of Tokyo, Japan
 +
  * Karin Verspoor, The University of Melbourne, Australia
 +
  * Byron C. Wallace,  University of Texas at Austin, USA
 +
  * W John Wilbur, US National Library of Medicine
 +
  * Pierre Zweigenbaum, LIMSI - CNRS, France
 +
 
 +
 
 +
 
 +
<h5>The BioNLP Shared Task (BioNLP-ST) and the BioASQ Challenge associated with the workshop</h5>
 +
 +
The <b>BioNLP Shared Task (BioNLP-ST)</b> has been organized three times so far, leading to the development of information extraction systems for molecular biology and medicine in 2009, 2011 and 2013. One of the major contributions of BioNLP-ST is the availability of resources such as high quality manually curated corpora, tools, and evaluation services.
 +
 +
<b>Shared Task Organizers:</b>
 +
 
 +
* Jin-Dong Kim, Database Center for Life Science (DBCLS), Japan
 +
* Claire Nedellec, INRA, France
 +
* Robert Bossy, INRA, France
 +
 
 +
 +
The second equally successful shared task, the <b>BioASQ challenge</b> on large-scale biomedical semantic indexing and question answering has been running on an annual basis since 2012. The results of the challenge were presented in a workshop, which has so far been taking place in conjunction with the CLEF conference and was extremely well-attended.
 +
 +
BioASQ assesses the performance of information systems in supporting two tasks that are central in the biomedical question answering process: (a) the indexing of large volumes of unlabeled data, primarily scientific articles, with biomedical concepts, (b) the processing of biomedical questions and the generation of answers and supporting material.  BioASQ has been making publicly available the following benchmark data and tools: more than 1300 questions and related answers, as well as online "oracle" for objective evaluation of any system throughout the year, not only during the challenge.
 +
 +
<b>BioASQ Organizers:</b>
 +
 +
* Georgios Paliouras NCSR "Demokritos", Greece and University of Houston, USA
 +
* Ioannis Kakadiaris University of Houston, USA
 +
* Anastasia Krithara, NCSR "Demokritos", Greece
 +
 +
-->
 +
 
 +
<!-- ==BIONLP 2015 Workshop Schedule==
 +
 
 +
<table cellspacing="0" cellpadding="5" border="0">
 +
<tr><td colspan=2><b>Thursday, July 30</b></td></tr>
 +
<tr><td style="width:10%; vertical-align:top">8:00&#8211;8:20</td><td valign=top><b> Opening remarks</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 1: Reading biomedical literature</b></td></tr>
 +
<tr><td valign=top>8:20&#8211;8:40</td><td>Complex Event Extraction using DRUM<br><em>James Allen<sup>1</sup>,&nbsp;Will de Beaumont<sup>2</sup>,&nbsp;Lucian Galescu<sup>2</sup>,&nbsp;Choh Man Teng<sup>2</sup></em><br><sup>1</sup>Institute for Human and Machine Cognition and University of Rochester, <sup>2</sup>Institute for Human and Machine Cognition</td></tr>
 +
<tr><td valign=top>8:40&#8211;9:00</td><td>Making the most of limited training data using distant supervision<br><em>Roland Roller and Mark Stevenson</em><br>University of Sheffield</td></tr>
 +
<tr><td valign=top>9:00&#8211;9:20</td><td>An extended dependency graph for relation extraction in biomedical texts<br><em>Yifan Peng<sup>1</sup>,&nbsp;Samir Gupta<sup>2</sup>,&nbsp;Cathy Wu<sup>2</sup>,&nbsp;Vijay Shanker<sup>2</sup></em><br><sup>1</sup>Computer and Information Sciences,  University of Delaware,&nbsp;<sup>2</sup>University of Delaware</td></tr>
 +
<tr><td valign=top>9:20&#8211;9:40</td><td>Event Extraction in pieces:Tackling the partial event identification problem on unseen corpora<br><em>Chrysoula Zerva and Sophia Ananiadou</em><br>University of Manchester</td></tr>
 +
<tr><td valign=top>9:40&#8211;10:00</td><td>Extracting Biological Pathway Models From NLP Event Representations<br><em>Michael Spranger<sup>1</sup>,&nbsp;Sucheendra Palaniappan<sup>2</sup>,&nbsp;Samik Ghosh<sup>3</sup></em><br><sup>1</sup>Sony Computer Science Laboratories Inc., <sup>2</sup>INRIA, Campus de Beaulieu, Rennes, France, <sup>3</sup>The Systems Biology Institute, Minato-ku, Tokyo, Japan</td></tr>
 +
<tr><td valign=top>10:00&#8211;10:20</td><td>Shallow Training is cheap but is it good enough? Experiments with Medical Fact Coding<br><em>Ramesh Nallapati and Radu Florian</em><br>IBM T. J. Watson Research Center</td></tr>
 +
<tr><td valign=top>10:30&#8211;11:00</td><td valign=top><b>Coffee Break</b></td></tr>
 +
<tr><td valign=top>11:00&#8211;11:45</td><td valign=top><b><font face="verdana" color="#000080"> Keynote: "Machine Reading: Attempting to model and understand biological processes" - Christopher Manning</font></b></td></tr>
 +
<tr><td valign=top>11:45&#8211;12:30</td><td valign=top><b><font face="verdana" color="#000080"> Keynote: “The DARPA Big Mechanism Program” - Kevin Knight</font></b></td></tr>
 +
<tr><td valign=top>12:30&#8211;14:00</td><td valign=top><b>Lunch</b></td></tr>
 +
<tr><td valign=top>14:00&#8211;15:00</td><td valign=top><b>Poster session</b></td></tr>
 +
<tr><td valign=top>15:00&#8211;15:30</td><td valign=top><b><font face="verdana" color="#000080"> Invited Talk: “Overview of BioCreative V Challenge Tasks” - Zhiyong Lu</font></b></td></tr>
 +
<tr><td valign=top>15:30&#8211;16:00</td><td valign=top><b>Coffee Break</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 2: Clinical text processing</b></td></tr>
 +
<tr><td valign=top>16:00&#8211;16:20</td><td>Stacked Generalization for Medical Concept Extraction from Clinical Notes<br><em>Youngjun Kim and Ellen Riloff</em><br>University of Utah</td></tr>
 +
<tr><td valign=top>16:20&#8211;16:40</td><td>Extracting Disease-Symptom Relationships by Learning Syntactic Patterns from Dependency Graphs<br><em>Mohsen Hassan<sup>1</sup>,&nbsp;Olfa Makkaoui<sup>2</sup>,&nbsp;Adrien Coulet<sup>1</sup>,&nbsp;Yannick Toussain<sup>1</sup></em><br><sup>1</sup>LORIA (CNRS, Inria NGE, Universit&eacute;  de Lorraine), <sup>2</sup>LORIA (Inria NGE)</td></tr>
 +
<tr><td valign=top>16:40&#8211;17:00</td><td>Extracting Time Expressions from Clinical Text<br><em>Timothy Miller<sup>1</sup>,&nbsp;Steven Bethard<sup>2</sup>,&nbsp;Dmitriy Dligach<sup>1</sup>,&nbsp;Chen Lin<sup>3</sup>,&nbsp;Guergana Savova<sup>4</sup></em><br><sup>1</sup>Boston Children's Hospital and Harvard Medical School, <sup>2</sup>University of Alabama at Birmingham, <sup>3</sup>Boston Children's Hospital, <sup>4</sup>Harvard</td></tr>
 +
<tr><td valign=top>17:00&#8211;17:20</td><td>Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion<br><em>Yue Liu<sup>1</sup>,&nbsp;Tao Ge<sup>2</sup>,&nbsp;Kusum Mathews<sup>3</sup>,&nbsp;Heng Ji<sup>1</sup>,&nbsp;Deborah McGuinness<sup>1</sup></em><br><sup>1</sup>Rensselaer Polytechnic Institute, <sup>2</sup>Key Laboratory of Computational Linguistics, Peking University, <sup>3</sup>Departments of Medicine and Emergency Medicine, Icahn School of Medicine at Mount Sinai</td></tr>
 +
<tr><td valign=top>17:20&#8211;17:40</td><td>Semantic Type Classification of Common Words in Biomedical Noun Phrases<br><em>Amy Siu and Gerhard Weikum</em><br>Max Planck Institute for Informatics</td></tr>
 +
<tr><td valign=top>17:40&#8211;18:00</td><td>CoMAGD: Annotation of Gene-Depression Relations<br><em>Rize Jin<sup>1</sup>,&nbsp;Jinseon You<sup>1</sup>,&nbsp;Jin-Woo Chung<sup>2</sup>,&nbsp;Hee-Jin Lee<sup>1</sup>,&nbsp;Maria Wolters<sup>3</sup>,&nbsp;Jong Park<sup>2</sup></em><br><sup>1</sup>Korea Advanced Institute of Science and Technology, <sup>2</sup>KAIST, <sup>3</sup>The University of Edinburgh</td></tr>
 +
<tr><td valign=top>18:00</td><td valign=top><b>Closing remarks</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Posters</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Lexical Characteristics Analysis of Chinese Clinical Documents<br><em>Meizhi Ju<sup>1</sup>,&nbsp;Haomin Li<sup>2</sup>,&nbsp;Huilong Duan<sup>1</sup></em><br><sup>1</sup>College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China., <sup>2</sup>The Children’s Hospital of Zhejiang University, Hangzhou, China;The Institute of Translational Medicine, Zhejiang University, Hangzhou, China.</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Using word embedding for bio-event extraction<br><em>Chen Li<sup>1</sup>,&nbsp;Runqing Song<sup>2</sup>,&nbsp;Maria Liakata<sup>3</sup>,&nbsp;Andreas Vlachos<sup>4</sup>,&nbsp;Stephanie Seneff<sup>1</sup>,&nbsp;Xiangrong Zhang<sup>2</sup></em><br><sup>1</sup>Massachusetts Institute of Technology, <sup>2</sup>Xidian University, <sup>3</sup>University of Warwick, <sup>4</sup>University College London</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Measuring the readability of medical research journal abstracts<br><em>Samuel J. Severance<sup>1</sup> and K. Bretonnel Cohen<sup>2</sup></em><br><sup>1</sup>School of Education, Institute of Cognitive Science, University of Colorado, <sup>2</sup>U. Colorado School of Medicine</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Translating Electronic Health Record Notes from English to Spanish: A Preliminary Study<br><em>Weisong Liu<sup>1</sup> and Shu Cai<sup>2</sup></em><br><sup>1</sup>UMass Medical School, <sup>2</sup>Information Sciences Institute</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Automatic Detection of Answers to Research Questions from Medline Abstracts<br><em>Abdulaziz Alamri<sup>1</sup> and Mark Stevenson<sup>2</sup></em><br><sup>1</sup>PhD Student, The university of Sheffield, <sup>2</sup>University of Sheffield</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>A preliminary study on automatic identification of patient smoking status in unstructured electronic health records<br><em>Jitendra Jonnagaddala<sup>1</sup>,&nbsp;Hong-Jie Dai<sup>2</sup>,&nbsp;Pradeep Ray<sup>1</sup>,&nbsp;Siaw-Teng Liaw<sup>1</sup></em><br><sup>1</sup>UNSW Australia, <sup>2</sup>Taipei Medical University</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Restoring the intended structure of Hungarian ophthalmology documents<br><em>Borbala Siklosi<sup>1</sup> and Attila Novak<sup>2</sup></em><br><sup>1</sup>Pazmany Peter Catholic University, <sup>2</sup>MTA-PPKE Hungarian Language Technology Research Group, Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, Budapest</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Evaluating distributed word representations for capturing semantics of biomedical concepts<br><em>MUNEEB TH<sup>1</sup>,&nbsp;Sunil Sahu<sup>2</sup>,&nbsp;Ashish Anand<sup>2</sup></em><br><sup>1</sup>IIT GUWAHATI, <sup>2</sup>Indian Institute of Technology Guwahati</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Investigating Public Health Surveillance using Twitter<br><em>Antonio Jimeno Yepes<sup>1</sup>,&nbsp;Andrew MacKinlay<sup>1</sup>,&nbsp;Bo Han<sup>2</sup></em><br><sup>1</sup>IBM, <sup>2</sup>IBM Research</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Clinical Abbreviation Disambiguation Using Neural Word Embeddings<br><em>Yonghui Wu<sup>1</sup>,&nbsp;Jun Xu<sup>2</sup>,&nbsp;Yaoyun Zhang<sup>3</sup>,&nbsp;Hua Xu<sup>1</sup></em><br><sup>1</sup>The University of Texas Health Science Center at Houston, <sup>2</sup>School of Biomedical Informatics, The University of Texas Health Science Center at Houston, <sup>3</sup>University of Texas School of Biomedical Informatics at Houston</td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top>Representing Clinical Diagnostic Criteria in Quality Data Model Using Natural Language Processing<br><em>Na Hong<sup>1</sup>,&nbsp;Dingcheng Li<sup>1</sup>,&nbsp;Yue Yu<sup>1</sup>,&nbsp;Hongfang Liu<sup>1</sup>,&nbsp;Christopher G. Chute<sup>2</sup>,&nbsp;Guoqian Jiang<sup>1</sup></em><br><sup>1</sup>Mayo Clinic, <sup>2</sup>Johns Hopkins University</td></tr>
 +
</table>
 +
 
 +
 
 +
==BIONLP 2015 Call for Papers==
 +
 
 +
Date of workshop: July 30, 2015
 +
 
 +
Venue: BioNLP 2015 will be held in conjunction with ACL 2015 in Beijing, People’s Republic of China.
 +
 
 +
===IMPORTANT DATES===
 +
* 14 May 2015: Submission due date
 +
* 4 June 2015: Notification of acceptance
 +
* 21 June 2015: Camera-ready papers due
 +
* 30 July 2015: Workshop, Beijing, PRC
 +
 
 +
 
 +
Over the course of the past thirteen years, the ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP and exposing these researchers to the mainstream ACL research, and a venue for informing the mainstream ACL researchers about this fast-growing and important domain. The workshop will continue presenting work on a broad and interesting range of topics in NLP.  Submissions are particularly solicited in the areas of:
 +
<ul>
 +
<li> Wide-scale entity identification and normalization </li>
 +
<li> Extraction of complex relations and events </li>
 +
<li> Discourse analysis </li>
 +
<li> Coreference resolution </li>
 +
<li> Text mining </li>
 +
<li> Literature based discovery </li>
 +
<li> Summarization </li>
 +
<li> Question-answering </li>
 +
<li> Lexical and terminological resources for BioNLP </li>
 +
<li> Annotation (corpora), standards </li>
 +
<li> Resources and strategies for system testing and evaluation </li>
 +
<li> Processing and annotation platforms </li>
 +
<li> Interoperable platforms for biomedical text mining </li>
 +
<li> Domain adaptation </li>
 +
<li> Translating NLP research to practice </li>
 +
</ul>
 +
 
 +
Submission instructions:
 +
 
 +
Long papers may consist of up to eight (8) pages of content, plus two pages for references; final versions of long papers will be given one additional page (up to 9 pages with 2 pages for references) so that reviewers’comments can be taken into account.
 +
 
 +
Poster submissions may consist of up to four (4) pages of content, plus 2 pages for references. Upon acceptance, short papers will be given five (5) pages in the proceedings and 2 pages for references. Authors are encouraged to use this additional page to address reviewers comments in their final versions.
 +
 
 +
https://www.softconf.com/acl2015/BioNLP/
 +
Authors who cannot submit a PDF file electronically should contact the workshop organizers well in advance of the submission deadline.
 +
Dual submission policy: note that papers may NOT be submitted to the BioNLP 2014 workshop if they are or will be concurrently submitted to another meeting or publication.
 +
Submissions should be anonymous.
 +
 
 +
-->
 +
<!--
 +
==BIONLP 2014 Workshop Schedule==
 +
Registration is now open (scroll to 2-Day Workshops): [http://acl2014.org/Registration.htm ACL 2014 Registration ]
 +
<table cellspacing="0" cellpadding="5" border="0"><tr><td colspan=2><h4>Thursday, June 26, 2014</h4></td></tr>
 +
<tr><td valign=top>9:00&#8211;9:10</td><td valign=top><b> Opening remarks</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 1: Processing biomedical publications</b></td></tr>
 +
<tr><td valign=top width=100>9:10&#8211;9:30</td><td valign=top align=left><i>Natural Language Processing Methods for Enhancing Geographic Metadata for Phylogeography of Zoonotic Viruses</i><br>
 +
Tasnia Tahsin, Robert Rivera, Rachel Beard, Rob Lauder, Davy Weissenbacher, Matthew Scotch, Garrick Wallstrom and Graciela Gonzalez</td></tr>
 +
<tr><td valign=top width=100>9:30&#8211;9:50</td><td valign=top align=left><i>Temporal Expression Recognition for Cell Cycle Phase Concepts in Biomedical Literature</i><br>
 +
Negacy Hailu, Natalya Panteleyeva and Kevin Cohen</td></tr>
 +
<tr><td valign=top width=100>9:50&#8211;10:10</td><td valign=top align=left><i>Classifying Negative Findings in Biomedical Publications</i><br>
 +
Bei Yu and Daniele Fanelli</td></tr>
 +
<tr><td valign=top width=100>10:10&#8211;10:30</td><td valign=top align=left><i>Automated Disease Normalization with Low Rank Approximations</i><br>
 +
Robert Leaman and Zhiyong Lu</td></tr>
 +
<tr><td valign=top>10:30&#8211;11:00</td><td valign=top><b>Coffee Break</b></td></tr>
 +
<tr><td valign=top>11:00&#8211;11:50</td><td valign=top><b>
 +
<font face="verdana" color="#000080"> Keynote by Junichi Tsujii: BioNLP as the Pioneering field of linking text, knowledge and data </font>
 +
</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 2: Processing consumer language</b></td></tr>
 +
<tr><td valign=top width=100>11:50&#8211;12:10</td><td valign=top align=left><i>Decomposing Consumer Health Questions</i><br>
 +
Kirk Roberts, Halil Kilicoglu, Marcelo Fiszman and Dina Demner-Fushman</td></tr>
 +
<tr><td valign=top width=100>12:10&#8211;12:30</td><td valign=top align=left><i>Detecting Health Related Discussions in Everyday Telephone Conversations for Studying Medical Events in the Lives of Older Adults</i><br>
 +
Golnar Sheikhshab, Izhak Shafran and Jeffrey Kaye</td></tr>
 +
<tr><td valign=top>12:30&#8211;2:00</td><td valign=top><b>Lunch</b></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 3: Processing clinical text and gray literature</b></td></tr>
 +
<tr><td valign=top width=100>2:00&#8211;2:20</td><td valign=top align=left><i>Coreference Resolution for Structured Drug Product Labels</i><br>
 +
Halil Kilicoglu and Dina Demner-Fushman</td></tr>
 +
<tr><td valign=top width=100>2:20&#8211;2:40</td><td valign=top align=left><i>Generating Patient Problem Lists from the ShARe Corpus using SNOMED CT/SNOMED CT CORE Problem List</i><br>
 +
Danielle Mowery, Mindy Ross, Sumithra Velupillai, Stephane Meystre, Janyce Wiebe and Wendy Chapman</td></tr>
 +
<tr><td valign=top width=100>2:40&#8211;3:00</td><td valign=top align=left><i>A System for Predicting ICD-10-PCS Codes from Electronic Health Records</i><br>
 +
Michael Subotin and Anthony Davis</td></tr>
 +
<tr><td valign=top width=100>3:00&#8211;3:20</td><td valign=top align=left><i>Structuring Operative Notes using Active Learning</i><br>
 +
Kirk Roberts, Sanda Harabagiu and Michael Skinner</td></tr>
 +
<tr><td valign=top>3:30-4:00</td><td valign=top><b>Afternoon Break</b></td></tr>
 +
<tr><td valign=top width=100>4:00&#8211;4:20</td><td valign=top align=left><i>Chunking Clinical Text Containing Non-Canonical Language</i><br>
 +
Aleksandar Savkov, John Carroll and Jackie Cassell</td></tr>
 +
<tr><td valign=top width=100>4:20&#8211;4:40</td><td valign=top align=left><i>Decision Style in a Clinical Reasoning Corpus</i><br>
 +
Limor Hochberg, Cecilia Ovesdotter Alm, Esa M. Rantanen, Caroline M. DeLong and Anne Haake</td></tr>
 +
<tr><td valign=top>4:40-5:30</td><td valign=top><b>Poster session</b></td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Temporal Expressions in Swedish Medical Text &#8211; A Pilot Study</i><br>
 +
Sumithra Velupillai</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>A repository of semantic types in the MIMIC II database clinical notes</i><br>
 +
Richard Osborne, Alan Aronson and Kevin Cohen</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Extracting drug indications and adverse drug reactions from Spanish health social media</i><br>
 +
Isabel Segura-Bedmar, Santiago de la Pe&ntilde;a Gonz&aacute;lez and Paloma Mart&iacute;nez</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Symptom extraction issue</i><br>
 +
Laure Martin, Delphine Battistelli and Thierry Charnois</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Seeking Informativeness in Literature Based Discovery</i><br>
 +
Judita Preiss</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach</i><br>
 +
Matthias Hartung, Roman Klinger, Matthias Zwick and Philipp Cimiano</td></tr>
 +
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>FFTM: A Fuzzy Feature Transformation Method for Medical Documents</i><br>
 +
Amir Karami and Aryya Gangopadhyay</td></tr>
 +
<tr><td colspan=2><h4>Friday, June 27, 2014</h4></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b>Session 1: NLP approaches for assessment of clinical conditions</b></td></tr>
 +
<tr><td valign=top width=100>9:00&#8211;9:40</td><td valign=top align=left><i>Using statistical parsing to detect agrammatic aphasia</i><br>
 +
Kathleen C. Fraser, Graeme Hirst, Jed A. Meltzer, Jennifer E. Mack and Cynthia K. Thompson
 +
<br>
 +
<font face="verdana" color="#000080"> Discussants: Thamar Solorio and Yang Liu </font></td></tr>
 +
<tr><td valign=top>&nbsp;</td><td valign=top><b><font face="verdana" color="#000080">Panel: Life cycles of BioCreative, BioNLP-ST, i2b2, TREC Medical tracks, and ShARe /CLEF/ SemEval</font></b></td></tr>
 +
<tr><td valign=top>9:40-10:05</td><td valign=top>BioCreative by Lynette Hirschman and John Wilbur</td></tr>
 +
<tr><td valign=top>10:05-10:30</td><td valign=top>BioNLP-ST by Sophia Ananiadou and Junichi Tsujii</td></tr>
 +
<tr><td valign=top>10:30-11:00</td><td valign=top><b>Coffee Break</b></td></tr>
 +
<tr><td valign=top>11:00-11:20</td><td valign=top>TREC Medical tracks by Ellen Voorhees</td></tr>
 +
<tr><td valign=top>11:20-11:25</td><td valign=top>Introduction to the TAC 2014 Biomedical Summarization Track by Lucy Vanderwende</td></tr>
 +
<tr><td valign=top>11:25-11:50</td><td valign=top>i2b2 by Ozlem Uzuner</td></tr>
 +
<tr><td valign=top>11:50-12:10</td><td valign=top>ShARe/CLEF/SemEval by Danielle Mowery, Sumithra Velupillai and Sameer Pradhan</td></tr>
 +
<tr><td valign=top>12:10-12:30</td><td valign=top>Discussion</td></tr>
 +
<tr><td valign=top>12:30-2:00</td><td valign=top><b>Lunch</b></td></tr>
 +
<tr><td valign=top>2:00-3:30</td><td valign=top><b><font face="verdana" color="#000080">Tutorial by Olivier Bodenreider: UMLS in biomedical text processing</font></b></td></tr>
 +
<tr><td valign=top>3:30-4:00</td><td valign=top>Afternoon Break</td></tr>
 +
<tr><td valign=top>4:00-5:30</td><td valign=top><b><font face="verdana" color="#000080">Tutorial by Alan R. Aronson: Using MetaMap</font></b></td></tr>
 +
</table>
 +
 
 +
 
 +
==BIONLP 2014 Call for Papers==
 +
 
 +
An ACL 2014 2-day Workshop associated with the [[SIGBIOMED]] special interest group Featuring a special track on NLP approaches for assessment of clinical conditions and a panel on shared tasks.
 
Baltimore, MD, June 26-27, 2014
 
Baltimore, MD, June 26-27, 2014
Workshop web site: http://aclweb.org/aclwiki/index.php?title=BioNLP_Workshop
 
 
  
 
===IMPORTANT DATES===
 
===IMPORTANT DATES===
Line 43: Line 1,409:
 
===KEYNOTE SPEAKER===
 
===KEYNOTE SPEAKER===
  
TBD
+
Jun-ichi Tsujii
  
 
===PANEL ON SHARED TASKS===
 
===PANEL ON SHARED TASKS===
Line 64: Line 1,430:
  
  
Short papers should not exceed four (4) pages including references.  
+
Short papers should not exceed four (4) pages plus at most 2 pages for references.  
 
Accepted short papers will be published in a separate section of the  
 
Accepted short papers will be published in a separate section of the  
 
workshop proceedings.  
 
workshop proceedings.  
Line 70: Line 1,436:
 
notes, descriptions of work in progress, etc.
 
notes, descriptions of work in progress, etc.
  
Submissions must be electronic and in PDF format, and should follow
+
Electronic Submission:
the two-column format of ACL proceedings. Please see the conference
+
Submission must be electronic and in PDF format, using the Softconf submission software at
website for details.
 
Authors are strongly encouraged to use the style files and formatting
 
instructions available on the conference website:
 
  
 
[https://www.softconf.com/acl2014/BioNLP  https://www.softconf.com/acl2014/BioNLP]
 
[https://www.softconf.com/acl2014/BioNLP  https://www.softconf.com/acl2014/BioNLP]
 +
 +
Submissions should follow the two-column format of ACL 2014 proceedings.
 +
Please see  the style files and formatting
 +
instructions at http://www.cs.jhu.edu/ACL2014/CallforPapers.htm
  
 
Submissions need to be anonymous.  
 
Submissions need to be anonymous.  
Line 97: Line 1,464:
 
   * Thamar Solorio, The University of Alabama at Birmingham  
 
   * Thamar Solorio, The University of Alabama at Birmingham  
 
   * Yang Liu, The University of Texas at Dallas
 
   * Yang Liu, The University of Texas at Dallas
 +
-->

Latest revision as of 09:33, 10 December 2023

SIGBIOMED

BIONLP 2023 and Shared Tasks @ ACL 2023

The 22nd BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with ACL 2023


IMPORTANT DATES

Video is optional. Instructions (below) are for the video only, not for the final paper submission. Video should not exceed 10 minutes.

Instructions:

 https://docs.google.com/presentation/d/1STKSZ22v3ucS9smfDfhREQhwRB9_bIwu7mnVYKUq7A8/edit?usp=sharing

Form (linked in SLIDE 4) https://acl2023workshops.paperform.co/


  • BioNLP 2023 Workshop at ACL, July 13, 2023, Toronto, Canada


Registration: https://2023.aclweb.org/registration/

VISA Information

ACL organizers are processing the requests.

Please see the instructions here: https://2023.aclweb.org/blog/visa-info/


Poster size:

All posters should be A0, orientation: Portrait.


BioNLP 2023: Program

Thursday July 13, 2023

Location: Pier 2 Ballroom
8:30–8:40 Opening remarks
 Session 1: Evaluating speech, models and literature-related tasks
8:40–9:00Evaluating and Improving Automatic Speech Recognition using Severity
Ryan Whetten and Casey Kennington, Boise State University
9:00–9:20Is the ranking of PubMed similar articles good enough? An evaluation of text similarity methods for three datasets
Mariana Neves, Ines Schadock, Beryl Eusemann, Gilbert Schönfelder, Bettina Bert, Daniel Butzke, German Federal Institute for Risk Assessment
9:20–9:40BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition (Online)
Vera Pavlova and Mohammed Makhlouf, rttl.ai
9:40–10:00Promoting Fairness in Classification of Quality of Medical Evidence/i>
Simon Suster1, Timothy Baldwin2, Karin Verspoor3, 1University of Melbourne, 2MBZUAI, 3RMIT University
10:00–10:30BioLaySumm 2023 Shared Task: Lay Summarisation of Biomedical Research Articles

Tomas Goldsack1, Zheheng Luo2, Qianqian Xie2, Carolina Scarton1, Matthew Shardlow3, Sophia Ananiadou2, Chenghua Lin1,

1University of Sheffield, 2University of Manchester, 3Manchester Metropolitan University/i>
10:30–11:00Coffee Break
 Session 2: Clinical Language Processing
11:00–11:40Invited Talk: Dementia Detection from Speech: New Developments and Future Directions
Speaker: Kathleen Fraser
11:40–12:10Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes

Yanjun Gao1, Dmitriy Dligach2, Timothy Miller3, Majid Afshar1,

1University of Wisconsin, 2Loyola University Chicago, 3Boston Children's Hospital and Harvard Medical School
12:10–12:40Overview of the RadSum23 Shared Task on Multi-modal and Multi-anatomical Radiology Report Summarization
Jean-Benoit Delbrouck, Maya Varma, Pierre Chambon, Curtis Langlotz, Stanford University
12:40–13:00RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

Dave Van Veen1, Cara Van Uden1, Maayane Attias1, Anuj Pareek1, Christian Bluethgen1, Malgorzata Polacin2, Wah Chiu1, Jean-Benoit Delbrouck1, Juan Zambrano Chaves1, Curtis Langlotz1, Akshay Chaudhari1, John Pauly1,

1Stanford University, 2Stanford University, ETH Zurich
13:00–14:30Lunch
14:00–17:45Onsite Poster Session 1
 How Much do Knowledge Graphs Impact Transformer Models for Extracting Biomedical Events?
Laura Zanella and Yannick Toussaint, LORIA, Université de Lorraine
 DISTANT: Distantly Supervised Entity Span Detection and Classification

Ken Yano1, Makoto Miwa2, Sophia Ananiadou3,

1The National Institute of Advanced Industrial Science and Technology, 2Toyota Technological Institute, 3University of Manchester
 Event-independent temporal positioning: application to French clinical text

Nesrine Bannour1, Bastien Rance2, Xavier Tannier3, Aurélie Névéol1,

1Université Paris Saclay, CNRS, LISN, 2INSERM, centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Paris Cité, AP-HP, HEGP, HeKa, Inria Paris, 3Sorbonne Université, Inserm, LIMICS
 AliBERT: A Pre-trained Language Model for French Biomedical Text

Aman Berhe1, Guillaume Draznieks2, Vincent Martenot2, Valentin Masdeu2, Lucas Davy2, Jean-Daniel Zucker3,

1SU/IRD UMMISCO & Quinten, 2Quinten, 3SU/IRD, UMMISCO
 Building a Corpus for Biomedical Relation Extraction of Species Mentions
Oumaima El Khettari, Solen Quiniou, Samuel Chaffron, Nantes Université - LS2N
 Automated Extraction of Molecular Interactions and Pathway Knowledge using Large Language Model, Galactica: Opportunities and Challenges

Gilchan Park1, Byung-Jun Yoon1, Xihaier Luo1, Vanessa López-Marrero1, Patrick Johnstone1, Shinjae Yoo2, Francis Alexander1, 1Brookhaven National Laboratory, 2BNL

 Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge
François Remy, Kris Demuynck, Thomas Demeester, Ghent University - imec
 Resolving Elliptical Compounds in German Medical Text
Niklas Kämmer1, Florian Borchert1, Silvia Winkler1, Gerard de Melo2, Matthieu-P. Schapranow1, 1Hasso Plattner Institute, University of Potsdam, 2HPI/University of Potsdam
 End-to-end clinical temporal information extraction with multi-head attention
Timothy Miller1, Steven Bethard2, Dmitriy Dligach3, Guergana Savova1, 1Boston Children's Hospital and Harvard Medical School, 2University of Arizona, 3Loyola University Chicago
 Intermediate Domain Finetuning for Weakly Supervised Domain-adaptive Clinical NER
Shilpa Suresh, Nazgol Tavabi, Shahriar Golchin, Leah Gilreath, Rafael Garcia-Andujar, Alexander Kim, Joseph Murray, Blake Bacevich, Ata Kiapour, Musculoskeletal Informatics Group, Boston Children's Hospital, Harvard Medical School
 Biomedical Language Models are Robust to Sub-optimal Tokenization
Bernal Jimenez Gutierrez, Huan Sun, Yu Su, The Ohio State University
 BioNART: A Biomedical Non-AutoRegressive Transformer for Natural Language Generation

Masaki Asada1 and Makoto Miwa2,

1National Institute of Advanced Industrial Science and Technology, 2Toyota Technological Institute
 Can Social Media Inform Dietary Approaches for Health Management? A Dataset and Benchmark for Low-Carb Diet
Skyler Zou, Xiang Dai, Grant Brinkworth, Pennie Taylor, Sarvnaz Karimi, CSIRO
 Hospital Discharge Summarization Data Provenance

Paul Landes1, Aaron Chaise2, Kunal Patel1, Sean Huang2, Barbara Di Eugenio1,

1University of Illinois at Chicago, 2Vanderbilt University
 Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers

Israt Jahan1, Md Tahmid Rahman Laskar2, Chun Peng1, Jimmy Huang1,

1York University, 2Dialpad Inc.
 Zero-Shot Information Extraction for Clinical Meta-Analysis using Large Language Models
David Kartchner1,3, Selvi Ramalingam2, Irfan Al-Hussaini3, Olivia Kronick3, Cassie Mitchell3, 1Enveda Biosciences, 2Emory University, 3Georgia Institute of Technology
 Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers
Shreya Chandrasekhar, Chieh-Yang Huang, Ting-Hao Huang, Penn State University
 Extracting Drug-Drug and Protein-Protein Interactions from Text using a Continuous Update of Tree-Transformers
Sudipta Singha Roy and Robert E. Mercer, The University of Western Ontario
 Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction

Simon Meoni1, Éric De la Clergerie2, Théo Ryffel3,

1Arkhn/INRIA, 2Iniria, 3Arkhn
15:30–16:00Coffee Break
14:30–17:45Virtual Session 1
 Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction

Yueling Li1, Sebastian Martschat1, Simone Paolo Ponzetto2,

1BASF SE, 2University of Mannheim
 Gaussian Distributed Prototypical Network for Few-shot Genomic Variant Detection
Jiarun Cao, Niels Peek, Andrew Renehan, Sophia Ananiadou, University of Manchester
 Boosting Radiology Report Generation by Infusing Comparison Prior

Sanghwan Kim1, Farhad Nooralahzadeh2, Morteza Rohanian2, Koji Fujimoto3, Mizuho Nishio3, Ryo Sakamoto3, Fabio Rinaldi4, Michael Krauthammer2,

1ETH Zürich, 2University of Zurich, 3Kyoto University Graduate School of Medicine, 4IDSIA, Swiss AI Institute
 Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints

Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji, Vinod Kumar, Bronner P. Gonçalves, Christiana Kartsonaki, ISARIC Clinical Characterisation Group, Laura Merson, David Clifton,

University of Oxford
 Zero-shot Temporal Relation Extraction with ChatGPT
Chenhan Yuan, Qianqian Xie, Sophia Ananiadou, University of Manchester
 Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media
Tianlin Zhang, Kailai Yang, Sophia Ananiadou, University of Manchester
 Exploring Drug Switching in Patients: A Deep Learning-based Approach to Extract Drug Changes and Reasons from Social Media
Mourad Sarrouti, Carson Tao, Yoann Mamy Randriamihaja, Sumitovant Biopharma
 An end-to-end neural model based on cliques and scopes for frame extraction in long breast radiology reports

Perceval Wajsburt1 and Xavier Tannier2,

1Sorbonne Université, 2Sorbonne Université, Inserm, LIMICS
 ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformers
Vinayak Arannil, Tomal Deb, Atanu Roy, Amazon
 Privacy Aware Question-Answering System for Online Mental Health Risk Assessment
Prateek Chhikara, Ujjwal Pasupulety, John Marshall, Dhiraj Chaurasia, Shweta Kumari, University of Southern California
 Multiple Evidence Combination for Fact-Checking of Health-Related Information
Pritam Deka, Anna Jurek-Loughrey, Deepak P, Queen's University Belfast
 Comparing and combining some popular NER approaches on Biomedical tasks
Harsh Verma, Sabine Bergler, Narjesossadat Tahaei, Concordia University
 Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health

Chandreen Liyanage1, Muskan Garg2, Vijay Mago1, Sunghwan Sohn2,

1Lakehead University, 2Mayo Clinic
 Distantly Supervised Document-Level Biomedical Relation Extraction with Neighborhood Knowledge Graphs
Takuma Matsubara, Makoto Miwa, Yutaka Sasaki, Toyota Technological Institute
 Biomedical Relation Extraction with Entity Type Markers and Relation-specific Question Answering
Koshi Yamada, Makoto Miwa, Yutaka Sasaki, Toyota Technological Institute
 Biomedical Document Classification with Literature Graph Representations of Bibliographies and Entities
Ryuki Ida, Makoto Miwa, Yutaka Sasaki, Toyota Technological Institute
 WeLT: Improving Biomedical Fine-tuned Pre-trained Language Models with Cost-sensitive Learning

Ghadeer Mobasher1,2, Wolfgang Müller2, Olga Krebs2, Michael Gertz1

1Heidelberg University, 2Heidelberg Institute for Theoretical Studies – HITS gGmbH
 Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Hongyi Yuan1, Keming Lu2, Zheng Yuan3, 1Tsinghua University, 2University of Southern California, 3Alibaba Group
14:00–17:45Onsite Shared Task Poster Session
 GRASUM at BioLaySumm Task 1: Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries
Domenic Rosati, scite
 Team:PULSAR at ProbSum 2023:PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models

Hao Li1, Yuping Wu1, Viktor Schlegel2, Riza Batista-Navarro1, Thanh-Tung Nguyen3, Abhinav Ramesh Kashyap2, Xiao-Jun Zeng1, Daniel Beck4, Stefan Winkler5, Goran Nenadic1,

1University of Manchester, 2ASUS AICS, 3ASUS, 4University of Melbourne, 5National University of Singapore
 CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
Potsawee Manakul, Yassir Fathullah, Adian Liusie, Vyas Raina, Vatsal Raina, Mark Gales, University of Cambridge
 shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation

Sanjeev Kumar Karn1, Rikhiya Ghosh2, Kusuma P2, Oladimeji Farri2,

1Siemens, 2Siemens Healthineers
 CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models

Mong Yuan Sim1, Xiang Dai2, Maciej Rybinski3, Sarvnaz Karimi3,

1The University of Adelaide, 2CSIRO Data61, 3CSIRO
 KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization

Gangwoo Kim1, Hajung Kim1, Lei Ji2, Seongsu Bae3, chanhwi kim4, Mujeen Sung1, Hyunjae Kim1, Kun Yan5, Eric Chang6, Jaewoo Kang1,

1Korea University, 2MSRA, 3KAIST, 4Korea University, DMIS, 5Beihang University, 6Kingtex
 IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation
Yu-Hsuan Wu, Ying-Jia Lin, Hung-Yu Kao, National Cheng Kung University
 MDC at BioLaySumm Task 1: Evaluating GPT Models for Biomedical Lay Summarization
Oisín Turbitt, Robert Bevan, Mouhamad Aboshokor, Medicines Discovery Catapult
  LHS712EE at BioLaySumm 2023: Using BART and LED to summarize biomedical research articles
Quancheng Liu, Xiheng Ren, V.G.Vinod Vydiswaran, University of Michigan
14:30–17:45Virtual Shared Task Poster Session
 TALP-UPC at ProbSum 2023: Fine-tuning and Data Augmentation Strategies for NER
Neil Torrero, Gerard Sant, Carlos Escolano, Universitat politècnica de catalunya
  Team Converge at ProbSum 2023: Abstractive Text Summarization of Patient Progress Notes
Gaurav Kolhatkar, Aditya Paranjape, Omkar Gokhale, Dipali Kadam, Pune Institute Of Computer Technology
  nav-nlp at RadSum23: Abstractive Summarization of Radiology Reports using BART Finetuning
Sri Macharla, Ashok Madamanchi, Nikhilesh Kancharla, IIT Roorkee at Roorkee
  APTSumm at BioLaySumm Task 1: Biomedical Breakdown, Improving Readability by Relevancy Based Selection
A.S. Poornash, Atharva Deshmukh, Archit Sharma, Sriparna Saha, Indian Institute of Technology Patna
 ISIKSumm at BioLaySumm Task 1: BART-based Summarization System Enhanced with Bio-Entity Labels
Cağla Colak and İlknur Karadeniz, Işık University
 DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language ModelsClinical Progress Note Summarization with Rules and Languague Models

Ming Liu1, Dan Zhang1, Weicong Tan2, He Zhang3

1Deakin University, 2Monash University, 3CNPIEC KEXIN LTD
 ELiRF-VRAIN at BioNLP Task 1B: Radiology Report Summarization

Vicent Ahuir Esteve, Encarna Segarra, Lluís Hurtado,

Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València
 SINAI at RadSum23: Radiology Report Summarization Based on Domain-Specific Sequence-To-Sequence Transformer Model

Mariia Chizhikova, Manuel Díaz-Galiano, L. Alfonso Ureña-López, M. Teresa Martín-Valdivia,

University of Jaén
 KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization

Jinge Wu1, Daqian Shi2, Abul Hasan1, Honghan Wu1,

1University College London, 2University of Trento
 e-Health CSIRO at RadSum23: Adapting a Chest X-Ray Report Generator to Multimodal Radiology Report Summarisation
Aaron Nicolson, Jason Dowling, Bevan Koopman, CSIRO
 UTSA-NLP at RadSum23: Multi-modal Retrieval-Based Chest X-Ray Report Summarization
Tongnian Wang, Xingmeng Zhao, Anthony Rios, University of Texas at San Antonio
 VBD-NLP at BioLaySumm Task 1: Explicit and Implicit Key Information Selection for Lay Summarization on Biomedical Long Documents
Phuc Phan, Tri Tran, Hai-Long Trieu, VinBigData, JSC
 NCUEE-NLP at BioLaySumm Task 2: Readability-Controlled Summarization of Biomedical Articles Using the PRIMERA Models
Chao-Yi Chen, Jen-Hao Yang, Lung-Hao Lee, National Central University
 Pathology Dynamics at BioLaySumm: the trade-off between Readability, Relevance, and Factuality in Lay Summarization
Irfan Al-Hussaini, Austin Wu, Cassie Mitchell, Georgia Institute of Technology
 IITR at BioLaySumm Task 1:Lay Summarization of BioMedical articles using Transformers
Venkat praneeth Reddy, Pinnapu Reddy Harshavardhan Reddy, Karanam Sai Sumedh, Raksha Sharma, Indian Institute of Technology,Roorkee
17:45-18:00 Closing remarks

BioNLP 2023 Invited Talk

Title: Dementia Detection from Speech: New Developments and Future Directions


Abstract: Diagnosing and treating dementia is a pressing concern as the global population ages. A growing number of publications in NLP tackle the question of whether we can use speech and language analysis to automatically detect signs of this devastating disease. However, the field of NLP has changed rapidly since the task was first proposed. In this talk, Dr. Kathleen Fraser will summarize the foundational approaches to dementia detection from speech, and then review how current approaches are building on and improving over the earlier work. Dr. Fraser will present several areas that she believes are promising future directions, and discuss preliminary work from her group specifically on the topic of multimodal machine learning for remote cognitive assessment.

Bio: Dr. Kathleen Fraser is a computer scientist in the Digital Technologies Research Centre at the National Research Council Canada. Her research focuses on the use of natural language processing (NLP) in healthcare applications, as well as assessing and mitigating social bias in artificial intelligence systems. Dr. Fraser received her PhD in computer science from the University of Toronto in 2016, and subsequently completed a post-doc at the University of Gothenburg, Sweden. She was named an MIT Rising Star in Electrical Engineering and Computer Science, and was awarded the Governor General's Gold Academic Medal in 2017. She also co-founded the start-up Winterlight Labs, later acquired by Cambridge Cognition. She has been a research officer at the National Research Council since 2018 and also holds a position as adjunct professor at Carleton University.


WORKSHOP OVERVIEW AND SCOPE

The BioNLP workshop associated with the ACL SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. The workshop is running every year since 2002 and continues getting stronger. BioNLP welcomes and encourages work on languages other than English, and inclusion and diversity. BioNLP truly encompasses the breadth of the domain and brings together researchers in bio- and clinical NLP from all over the world. The workshop will continue presenting work on a broad and interesting range of topics in NLP. The interest to biomedical language has broadened significantly due to the COVID-19 pandemic and continues to grow: as access to information becomes easier and more people generate and access health-related text, it becomes clearer that only language technologies can enable and support adequate use of the biomedical text.

BioNLP 2023 will be particularly interested in language processing that supports DEIA (Diversity, Equity, Inclusion and Accessibility). The work on detection and mitigation of bias and misinformation continues to be of interest. Research in languages other than English, particularly, under-represented languages, and health disparities are always of interest to BioNLP.

Other active areas of research include, but are not limited to:

  • Tangible results of biomedical language processing applications;
  • Entity identification and normalization (linking) for a broad range of semantic categories;
  • Extraction of complex relations and events;
  • Discourse analysis;
  • Anaphora/coreference resolution;
  • Text mining / Literature based discovery;
  • Summarization;
  • Τext simplification;
  • Question Answering;
  • Resources and strategies for system testing and evaluation;
  • Infrastructures and pre-trained language models for biomedical NLP (Processing and annotation platforms);
  • Development of synthetic data & data augmentation;
  • Translating NLP research into practice;
  • Getting reproducible results.


SUBMISSION INSTRUCTIONS

Two types of submissions are invited: full (long) papers and short papers.

Submission site for the workshop only: https://softconf.com/acl2023/BioNLP2023/

Shared task participants' reports should be submitted at https://softconf.com/acl2023/BioNLP2023-ST.

The reports on the shared task participation will be reviewed by the task organizers.

Publication chairs for the tasks:

  • 1A: Yanjun Gao
  • 1B: Jean Benoit Delbrouck
  • 2: Chenghua Lin, Tomas Goldsack

Full (long) papers should not exceed eight (8) pages of text, plus unlimited references. Final versions of full papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account. Full papers are intended to be reports of original research.

BioNLP aims to be the forum for interesting, innovative, and promising work involving biomedicine and language technology, whether or not yielding high performance at the moment. This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation. Both types of research and combinations thereof are encouraged.

Short papers may consist of up to four (4) pages of content, plus unlimited references. Upon acceptance, short papers will still be given up to five (5) content pages in the proceedings. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.


Electronic Submission

Submissions must be electronic and in PDF format, using the Softconf START conference management system at https://softconf.com/acl2023/BioNLP2023/

We strongly recommend consulting the ACL Policies for Submission, Review, and Citation: https://2023.aclweb.org/calls/main_conference/ and using ACL LaTeX style files tailored for this year's conference. Submissions must conform to the official style guidelines: https://2023.aclweb.org/calls/style_and_formatting/

Submissions need to be anonymous.

Dual submission policy: papers may NOT be submitted to the BioNLP 2023 workshop if they are or will be concurrently submitted to another meeting or publication.

Program Committee

 * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK 
 * Emilia Apostolova, Anthem, Inc., USA
 * Eiji Aramaki, University of Tokyo, Japan 
 * Saadullah Amin, Saarland University, Germany
 * Steven Bethard, University of Arizona, USA
 * Olivier Bodenreider, US National Library of Medicine 
 * Robert Bossy, Inrae, Université Paris Saclay, France
 * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain
 * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA 
 * Brian Connolly, Ohio, USA
 * Mike Conway, University of Melbourne, Australia
 * Manirupa Das, Amazon, USA
 * Berry de Bruijn, National Research Council, Canada
 * Dina Demner-Fushman, US National Library of Medicine 
 * Bart Desmet, National Institutes of Health, USA
 * Dmitriy Dligach, Loyola University Chicago, USA
 * Kathleen C.	Fraser, National Research Council Canada
 * Travis Goodwin, Amazon Web Services (AWS), Seattle, Washington, USA
 * Natalia Grabar, CNRS, U Lille, France
 * Cyril Grouin, Université Paris-Saclay, CNRS
 * Tudor Groza, EMBL-EBI
 * Deepak Gupta, US National Library of Medicine 
 * William Hogan, UCSD, USA
 * Thierry Hamon, LIMSI-CNRS, France
 * Richard Jackson, AstraZeneca
 * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 * Sarvnaz Karimi, CSIRO, Australia
 * Nazmul Kazi, University of North Florida, USA
 * Roman Klinger, University of Stuttgart, Germany
 * Anna Koroleva, Omdena
 * Majid Latifi, Department of Computer Science, University of York, York, UK
 * Andre Lamurias, Aalborg University, Denmark
 * Alberto Lavelli, FBK-ICT, Italy
 * Robert Leaman, US National Library of Medicine 
 * Lung-Hao Lee, National Central University, Taiwan
 * Ulf Leser, Humboldt-Universität zu Berlin, Germany 
 * Timothy Miller, Boston Childrens Hospital and Harvard Medical School, USA
 * Claire Nedellec, French national institute of agronomy (INRA)
 * Guenter Neumann, German Research Center for Artificial Intelligence (DFKI)
 * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany
 * Nhung Nguyen, National Centre for Text Mining, University of Manchester, UK
 * Aurélie Névéol, CNRS, France
 * Amandalynne	Paullada, University of Washington School of Medicine
 * Yifan Peng,  Weill Cornell Medical College, USA
 * Laura Plaza, Universidad Nacional de Educación a Distancia
 * Francisco J. Ribadas-Pena, University of Vigo, Spain
 * Anthony Rios, The University of Texas at San Antonio, USA
 * Kirk Roberts, The University of Texas Health Science Center at Houston, USA 
 * Roland Roller, DFKI, Germany
 * Mourad Sarrouti, Sumitovant Biopharma, Inc., USA
 * Diana Sousa, University of Lisbon, Portugal
 * Peng Su, University of Delaware, USA
 * Madhumita Sushil, University of California, San Francisco, USA
 * Mario Sänger, Humboldt Universität zu Berlin, Germany
 * Andrew Taylor, Yale University School of Medicine, USA
 * Karin Verspoor, RMIT University, Australia
 * Leon Weber, Humboldt Universität Berlin, Germany
 * Nathan M. White, James Cook University, Australia
 * Dustin Wright, University of Copenhagen,Denmark
 * Amelie Wührl,  University of Stuttgart, Germany
 * Dongfang Xu, Harvard University, USA
 * Jingqing Zhang,  Imperial College London, UK
 * Ayah Zirikly, Johns Hopkins Whiting School of Engineering, USA
 * Pierre Zweigenbaum, LIMSI - CNRS, France


Organizers

 * Dina Demner-Fushman, US National Library of Medicine
 * Kevin Bretonnel Cohen, University of Colorado School of Medicine
 * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
 * Jun-ichi Tsujii, National Institute of Advanced Industrial Science and Technology, Japan

SHARED TASKS 2023

Shared Tasks on Summarization of Clinical Notes and Scientific Articles

The first task focuses on Clinical Text.

Task 1A. Problem List Summarization

Codalab competition for Problem List Summarization Evaluation: https://codalab.lisn.upsaclay.fr/competitions/12388 Test Set Release: https://physionet.org/content/bionlp-workshop-2023-task-1a/1.1.0/


The deadline for registration is March 1st, after which no further registrations will be accepted.

Automatically summarizing patients’ main problems from the daily care notes in the electronic health record can help mitigate information and cognitive overload for clinicians and provide augmented intelligence via computerized diagnostic decision support at the bedside. The task of Problem List Summarization aims to generate a list of diagnoses and problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization.This task aims to promote NLP model development for downstream applications in diagnostic decision support systems that could improve efficiency and reduce diagnostic errors in hospitals. This task will contain 768 hospital daily progress notes and 2783 diagnoses in the training set, and a new set of 300 daily progress notes will be annotated by physicians as the test set. The annotation methods and annotation quality have previously been reported here. The goal of this shared task is to attract future research efforts in building NLP models for real-world decision support applications, where a system generating relevant and accurate diagnoses will assist the healthcare providers’ decision-making process and improve the quality of care for patients.


Shared Task 1A Registration: https://forms.gle/yp6TKD66G8KGpweN9

Please join our Google discussion group for the important update: https://groups.google.com/g/bionlp2023problemsumm

Full Task 1A Details at:

https://physionet.org/content/bionlp-workshop-2023-task-1a/1.0.0/


Important Dates:

  • Registration Started: January 13th, 2023
  • Releasing of training and validation data: January 13th, 2023
  • Registration stops: March 1, 2023
  • Releasing of test data: April 13th, 2023

Codalab competition for Problem List Summarization Evaluation: https://codalab.lisn.upsaclay.fr/competitions/12388 Test Set Release: https://physionet.org/content/bionlp-workshop-2023-task-1a/1.1.0/

  • System submission deadline: April 20th, 2023
  • System papers due date: April 28th, 2023
  • Notification of acceptance: June 1st, 2023
  • Camera-ready system papers due: June 6, 2023
  • BioNLP Workshop Date: July 13th, 2023


Task 1A Organizers:

  • Majid Afshar, Department of Medicine University of Wisconsin - Madison.
  • Yanjun Gao, University of Wisconsin Madison.
  • Dmitriy Dligach, Department of Computer Science at Loyola University Chicago.
  • Timothy Miller, Boston Children’s Hospital and Harvard Medical School.
Task 1B. Radiology report summarization

Radiology report summarization is a growing area of research. Given the Findings and/or Background sections of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study.

The research area of radiology report summarization currently faces an important limitation: most research is carried out on chest X-rays. To palliate these limitations, we propose two datasets: A shared summarization task that includes six different modalities and anatomies, totalling 79,779 samples, based on the MIMIC-III database.

A shared summarization task on chest x-ray radiology reports with images and a brand new out-of-domain test-set from Stanford.

Full Task 1B details at:

https://vilmedic.app/misc/bionlp23/sharedtask

Task 1B Organizers:

  • Jean-Benoit Delbrouck, Stanford University.
  • Maya Varma, Stanford University.


Task 2. Lay Summarization of Biomedical Research Articles

Biomedical publications contain the latest research on prominent health-related topics, ranging from common illnesses to global pandemics. This can often result in their content being of interest to a wide variety of audiences including researchers, medical professionals, journalists, and even members of the public. However, the highly technical and specialist language used within such articles typically makes it difficult for non-expert audiences to understand their contents.

Abstractive summarization models can be used to generate a concise summary of an article, capturing its salient point using words and sentences that aren’t used in the original text. As such, these models have the potential to help broaden access to highly technical documents when trained to generate summaries that are more readable, containing more background information and less technical terminology (i.e., a “lay summary”).

This shared task surrounds the abstractive summarization of biomedical research articles, with an emphasis on controllability and catering to non-expert audiences. Through this task, we aim to help foster increased research interest in controllable summarization that helps broaden access to technical texts and progress toward more usable abstractive summarization models in the biomedical domain.

For more information on Task 2, see:

Detailed descriptions of the motivation, the tasks, and the data are also published in:

  • Goldsack, T., Zhang, Z., Lin, C., Scarton, C.. Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature. EMNLP 2022.
  • Luo, Z., Xie, Q., Ananiadou, S.. Readability Controllable Biomedical Document Summarization. EMNLP 2022 Findings.


Task 2 Organizers:

  • Chenghua Lin, Deputy Director of Research and Innovation in the Computer Science Department, University of Sheffield.
  • Sophia Ananiadou, Turing Fellow, Director of the National Centre for Text Mining and Deputy Director of the Institute of Data Science and AI at the University of Manchester.
  • Carolina Scarton, Computer Science Department at the University of Sheffield.
  • Qianqian Xie, National Centre for Text Mining (NaCTeM).
  • Tomas Goldsack, University of Sheffield.
  • Zheheng Luo, the University of Manchester.
  • Zhihao Zhang, Beihang University.