BioNLP 2024
BIONLP 2024 and Shared Tasks @ ACL 2024
The 23rd BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with ACL 2024
IMPORTANT DATES
- Paper submission deadline: May 17 (Friday), 2024
- Notification of acceptance: June 24 (Tuesday), 2024
- Camera-ready paper due: July 5 (Friday), 2024 -- No extensions due to ACL publication deadline.
- Workshop: August 16, 2024, Location: TBA
For the in-person poster presentation, all posters are to be printed in this format:
A0 format (84.1 x 118.9 cm, / 33.1” x 46.8”) in Portrait / Vertical format.
The 23rd Workshop on Biomedical Natural Language Processing
PROGRAM
Friday, August 16, 2024 | |
| 08:15–08:30 Opening remarks | |
| 08:30–10:30 Session 1: Oral Presentations | |
| 08:30–08:50 | Improving Self-training with Prototypical Learning for Source-Free Domain Adaptation on Clinical Text Seiji Shimizu, Shuntaro Yada, Lisa Raithel and Eiji ARAMAKI |
| 08:50–09:10 | Generation and Evaluation of Synthetic Endoscopy Free-Text Reports with Differential Privacy Agathe Zecevic, Xinyue Zhang, Sebastian Zeki and Angus Roberts |
| 09:10–09:30 | Evaluating the Robustness of Adverse Drug Event Classification Models using Templates Dorothea MacPhail, David Harbecke, Lisa Raithel and Sebastian Möller |
| 09:30–09:50 | Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification Himanshu Gautam Pandey, Akhil Amod and Shivang Kumar |
| 09:50–10:10 | Open (Clinical) LLMs are Sensitive to Instruction Phrasings Alberto Mario Ceballos-Arroyo, Monica Munnangi, Jiuding Sun, Karen Zhang, Jered McInerney, Byron C. Wallace and Silvio Amir |
| 10:10–10:30 | Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency Vasiliki Kougia, Anastasiia Sedova, Andreas Joseph Stephan, Klim Zaporojets and Benjamin Roth |
| 10:30–11:00 | Coffee Break |
| 11:00–13:00 Session 2: Shared Tasks | |
| 11:00–11:20 | Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!" Justin Xu, Zhihong Chen, Andrew Johnston, Louis Blankemeier, Maya Varma, Jason Hom, William J. Collins, Ankit Modi, Robert Lloyd, Benjamin Hopkins, Curtis Langlotz and Jean-Benoit Delbrouck |
| 11:20–11:25 | e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation Aaron Nicolson, Jinghui Liu, Jason Dowling, Anthony Nguyen and Bevan Koopman |
| 11:25–11:30 | WisPerMed at "Discharge Me!”: Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV Hendrik Damm, Tabea Margareta Grace Pakull, Bahadır Eryılmaz, Helmut Becker, Ahmad Idrissi-Yaghir, Henning Schäfer, Sergej Schultenkämper and Christoph M. Friedrich |
| 11:30–11:50 | Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles Tomas Goldsack, Carolina Scarton, Matthew Shardlow and Chenghua Lin |
| 11:50–12:00 | UIUC_BioNLP at BioLaySumm: An Extract-then-Summarize Approach Augmented with Wikipedia Knowledge for Biomedical Lay Summarization Zhiwen You, Shruthan Radhakrishna, Shufan Ming and Halil Kilicoglu |
| 12:00–12:20 | Shared Tasks Discussion |
| 12:20–13:00 | Invited Talk – Titipat Achakulvisut: Enhancing Neuroscience Conferences through Natural Language Processing |
| 13:00–14:30 | Lunch |
| 14:00–15:30 BioNLP Poster Session | |
| End-to-End Relation Extraction of Pharmacokinetic Estimates from the Scientific Literature Ferran Gonzalez Hernandez, Victoria Smith, Quang Nguyen, Palang Chotsiri, Thanaporn Wattanakul, José Antonio Cordero, Maria Rosa Ballester, Albert Sole, Gill Mundin, Watjana Lilaonitkul, Joseph F. Standing and Frank Kloprogge | |
| KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques Rui Yang, Haoran Liu, Edison Marrese-Taylor, Qingcheng Zeng, Yuhe Ke, Wanxin Li, Lechao Cheng, Qingyu Chen, James Caverlee, Yutaka Matsuo and Irene Li | |
| MedExQA: Medical Question Answering Benchmark with Multiple Explanations Yunsoo Kim, Jinge Wu, Yusuf Abdulle and Honghan Wu | |
| Do Clinicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation Zonghai Yao, Ahmed Jaafar, Beining Wang, Zhichao Yang and hong yu | |
| Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification? Aman Sinha, Timothee Mickus, Marianne Clausel, Mathieu Constant and Xavier Coubez | |
| Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology. Panagiotis Fytas, Anna Breger, Ian Selby, Simon Baker, Shahab Shahipasand and Anna Korhonen | |
| Using Large Language Models to Evaluate Biomedical Query-Focused Summarisation Hashem Hijazi, Diego Molla, Vincent Nguyen and Sarvnaz Karimi | |
| Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records Mireia Hernandez Caralt, Clarence Boon Liang Ng and Marek Rei | |
| Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension Shubham Vatsal and Ayush Singh | |
| Get the Best out of 1B LLMs: Insights from Information Extraction on Clinical Documents Saeed Farzi, Soumitra Ghosh, Alberto Lavelli and Bernardo Magnini | |
| K-QA: A Real-World Medical Q&A Benchmark Itay Manes, Naama Ronn, David Cohen, Ran Ilan Ber, Zehavi Horowitz-Kugler and Gabriel Stanovsky | |
| Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes Vahan Arsenyan, Spartak Bughdaryan, Fadi Shaya, Kent Wilson Small and Davit Shahnazaryan | |
| Document-level Clinical Entity and Relation extraction via Knowledge Base-Guided Generation Kriti Bhattarai, Inez Y. Oh, Zachary B. Abrams and Albert M. Lai | |
| BiCAL: Bi-directional Contrastive Active Learning for Clinical Report Generation Tianyi Wu, Jingqing Zhang, Wenjia Bai and Kai Sun | |
| Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs Sanjeet Singh, Shreya Gupta, Niralee Gupta, Naimish Sharma, Lokesh Srivastava, Vibhu Agarwal and Ashutosh Modi | |
| Pre-training data selection for biomedical domain adaptation using journal impact metrics Mathieu LAI-KING and Patrick Paroubek | |
| Leveraging LLMs and Web-based Visualizations for Profiling Bacterial Host Organisms and Genetic Toolboxes Gilchan Park, Vivek Mutalik, Christopher Neely, Carlos Soto, Shinjae Yoo and Paramvir Dehal | |
| REAL: A Retrieval-Augmented Entity Linking Approach for Biomedical Concept Recognition Darya Shlyk, Tudor Groza, Marco Mesiti, Stefano Montanelli and Emanuele Cavalleri | |
| Is That the Right Dose? Investigating Generative Language Model Performance on Veterinary Prescription Text Analysis Brian Hur, Lucy Lu Wang, Laura Hardefeldt and Meliha Yetisgen | |
| MiDRED: An Annotated Corpus for Microbiome Knowledge Base Construction William Hogan, Andrew Bartko, Jingbo Shang and Chun-Nan Hsu | |
| Do Numbers Matter? Types and Prevalence of Numbers in Clinical Texts Rahmad Mahendra, Damiano Spina, Lawrence Cavedon and Karin Verspoor | |
| A Fine-grained citation graph for biomedical academic papers: the finding-citation graph Yuan Liang, Massimo Poesio and Roonak Rezvani | |
| Evaluating Large Language Models for Predicting Protein Behavior under Radiation Exposure and Disease Conditions Ryan Engel and Gilchan Park | |
| XrayGPT: Chest Radiographs Summarization using Large Medical Vision-Language Models Omkar Chakradhar Thawakar, Abdelrahman M. Shaker, Sahal Shaji Mullappilly, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Jorma Laaksonen and Fahad Khan | |
| Multilevel Analysis of Biomedical Domain Adaptation of Llama 2: What Matters the Most? A Case Study Vicente Ivan Sanchez Carmona, Shanshan Jiang, Takeshi Suzuki and Bin Dong | |
| Mention-Agnostic Information Extraction for Ontological Annotation of Biomedical Articles Oumaima El Khettari, Noriki Nishida, Shanshan Liu, Rumana Ferdous Munne, Yuki Yamagata, Solen Quiniou, Samuel Chaffron and Yuji Matsumoto | |
| Automatic Extraction of Disease Risk Factors from Medical Publications Maxim Rubchinsky, Ella Rabinovich, Adi Shribman, Netanel Golan, Tali Sahar and Dorit Shweiki | |
| Intervention extraction in preclinical animal studies of Alzheimer’s Disease: Enhancing regex performance with language model-based filtering YIYUAN PU, Kaitlyn Hair, Daniel Beck, Mike Conway, Malcolm MacLeod and Karin Verspoor | |
| Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques Akshit Achara, Sanand Sasidharan and Gagan N | |
| XAI for Better Exploitation of Text in Medical Decision Support Ajay Madhavan Ravichandran, Julianna Grune, Nils Feldhus, Aljoscha Burchardt, Roland Roller and Sebastian Möller | |
| Optimizing Multimodal Large Language Models for Detection of Alcohol Advertisements via Adaptive Prompting Daniel Cabrera Lozoya, Jiahe Liu, Simon D’Alfonso and Mike Conway | |
| Extracting Epilepsy Patient Data with Llama 2 Ben Holgate, Shichao Fang, Anthony Shek, Matthew McWilliam, Pedro Viana, Joel S. Winston, James T. Teo and Mark P. Richardson | |
| How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical Questions Bojana Bašaragin, Adela Ljajić, Darija Medvecki, Lorenzo Cassano, Miloš Košprdić and Nikola Milošević | |
| Low Resource ICD Coding of Hospital Discharge Summaries Ashton Williamson, David de Hilster, Amnon Meyers, Nina Hubig and Amy Apon | |
| Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios Faraz Maschhur, Klaus Netter, Sven Schmeier, Katrin Ostermann, Rimantas Palunis, Tobias Strapatsas and Roland Roller | |
| Creating Ontology-annotated Corpora from Wikipedia for Medical Named-entity Recognition Johann Frei and Frank Kramer | |
| Paragraph Retrieval for Enhanced Question Answering in Clinical Documents Vojtech Lanz and Pavel Pecina | |
| CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification (ACL Findings) Korbinian Randl, John Pavlopoulos, Aron Henriksson and Tony Lindgren | |
| 15:30–16:00 | Coffee Break |
| 16:00–17:50 Shared Tasks Poster Session | |
| RRG24 | |
| CID at RRG24: Attempting in a Conditionally Initiated Decoding of Radiology Report Generation with Clinical Entities Yuxiang Liao, Yuanbang Liang, Yipeng Qin, Hantao Liu and Irena Spasic | |
| MAIRA at RRG24: A specialised large multimodal model for radiology report generation Shaury Srivastav, Mercy Ranjit, Fernando Pérez-García, Kenza Bouzid, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Harshita Sharma, Maximilian Ilse, Valentina Salvatelli, Sam Bond-Taylor, Fabian Falck, Anja Thieme, Hannah Richardson, Matthew P. Lungren, Stephanie L. Hyland and Javier Alvarez-Valle | |
| AIRI at RRG24: LLaVa with specialised encoder and decoder Marina Munkhoeva, Dmitry Umerenkov and Valentin Samokhin | |
| iHealth-Chile-1 at RRG24: In-context Learning and Finetuning of a Large Multimodal Model for Radiology Report Generation Diego Campanini, Oscar Loch, Pablo Messina, Rafael Elberg and Denis Parra | |
| iHealth-Chile-3&2 at RRG24: Template Based Report Generation Oscar Loch, Pablo Messina, Rafael Elberg, Diego Campanini, Álvaro Soto, René Vidal and Denis Parra | |
| Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation Xi Zhang, Zaiqiao Meng, Jake Lever and Edmond S.L. Ho | |
| SICAR at RRG2024: GPU Poor’s Guide to Radiology Report Generation Kiartnarin Udomlapsakul, Parinthapat Pengpun, Tossaporn Saengja, Kanyakorn Veerakanjana, Krittamate Tiankanon, Pitikorn Khlaisamniang, Pasit Supholkhan, Amrest Chinkamol, Pubordee Aussavavirojekul, Hirunkul Phimsiri, tara sripo, Chiraphat Boonnag, Trongtum Tongdee, Thanongchai Siriapisith, Pairash Saiviroonporn, Jiramet Kinchagawat and Piyalitt Ittichaiwong | |
| Discharge Me! | |
| Shimo Lab at “Discharge Me!”: Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections Yunzhen He, Hiroaki Yamagiwa and Hidetoshi Shimodaira | |
| Ixa-Med at Discharge Me! Retrieval-Assisted Generation for Streamlining Discharge Documentation Jordan C. Koontz, Maite Oronoz and Alicia Pérez | |
| QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM Rui Guo, Greg Farnan, Niall McLaughlin and Barry Devereux | |
| e-Health CSIRO at "Discharge Me!" 2024: Generating Discharge Summary Sections with Fine-tuned Language Models Jinghui Liu, Aaron Nicolson, Jason Dowling, Bevan Koopman and Anthony Nguyen | |
| UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models Mengxian Lyu, Cheng Peng, Daniel Paredes, Ziyi Chen, Aokun Chen, Jiang Bian and Yonghui Wu | |
| EPFL-MAKE at "Discharge Me!": An LLM System for Automatically Generating Discharge Summaries of Clinical Electronic Health Record Haotian Wu, Paul Boulenger, Antonin Faure, Berta Céspedes, Farouk Boukil, Nastasia Morel, Zeming Chen and Antoine Bosselut | |
| UoG Siephers at "Discharge Me!": Exploring Ways to Generate Synthetic Patient Notes From Multi-Part Electronic Health Records Erlend Frayling, Jake Lever and Graham McDonald | |
| Roux-lette at "Discharge Me!": Reducing EHR Chart Burden with a Simple, Scalable, Clinician-Driven AI Approach Suzanne Wendelken, Anson Antony, Rajashekar Korutla, Bhanu Pachipala, James Shanahan and Walid Saba | |
| Yale at "Discharge Me!": Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information Vimig Socrates, Thomas Huang, Xuguang Ai, Soraya Fereydooni, Qingyu Chen, R Andrew Taylor and David Chartash | |
| IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries An Quang Tang, Xiuzhen Zhang and Minh Ngoc Dinh | |
| MLBMIKABR at "Discharge Me!": Concept Based Clinical Text Description Generation Abir Naskar, Jane Hocking, Patty Chondros, Douglas Boyle and Mike Conway | |
| BIoLaySum | |
| DeakinNLP at BioLaySumm: Evaluating Fine-tuning Longformer and GPT-4 Prompting for Biomedical Lay Summarization Huy Quoc To, Ming Liu and Guangyan Huang | |
| ELiRF-VRAIN at BioLaySumm: Boosting Lay Summarization Systems Performance with Ranking Models Vicent Ahuir, Diego Torres, Encarna Segarra and Lluís-F. Hurtado | |
| BioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation Akanksha Karotia and Seba Susan | |
| WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles Tabea Margareta Grace Pakull, Hendrik Damm, Ahmad Idrissi-Yaghir, Henning Schäfer, Peter A. Horn and Christoph M. Friedrich | |
| HULAT-UC3M at BiolaySumm: Adaptation of BioBART and Longformer models to summarizing biomedical documents Adrian Gonzalez Sanchez and Paloma Martínez | |
| Saama Technologies at BioLaySumm: Abstract based fine-tuned models with LoRA Hwanmun Kim, Kamal raj Kanakarajan and Malaikannan Sankarasubbu | |
| AUTH at BioLaySumm 2024: Bringing Scientific Content to Kids Loukritia Stefanou, Tatiana Passali and Grigorios Tsoumakas | |
| SINAI at BioLaySumm: Self-Play Fine-Tuning of Large Language Models for Biomedical Lay Summarisation Mariia Chizhikova, Manuel Carlos Díaz-Galiano, L. Alfonso Ureña-López and María-Teresa Martín-Valdivia | |
| RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts Yuelyu Ji, Zhuochun Li, Rui Meng, Sonish Sivarajkumar, Yanshan Wang, Zeshui Yu, Hui Ji, Yushui Han, Hanyu Zeng and Daqing He | |
| Team YXZ at BioLaySumm: Adapting Large Language Models for Biomedical Lay Summarization Jieli Zhou, Cheng Ye, Pengcheng Xu and Hongyi Xin | |
| Eulerian at BioLaySumm: Preprocessing Over Abstract is All You Need Satyam Modi and T Karthikeyan | |
| HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers Hemang Malik, Gaurav Pradeep and Pratinav Seth | |
| Ctyun AI at BioLaySumm: Enhancing Lay Summaries of Biomedical Articles Through Large Language Models and Data Augmentation siyu bao, ruijing zhao, Siqin Zhang, jinghui zhang, weiyin wang and yunian ru | |
| 17:50–18:00 Closing remarks | |
SUBMISSION INSTRUCTIONS
Two types of submissions are invited: full (long) papers (8 pages) and short papers (4 pages).
Submission site for the workshop https://softconf.com/acl2024/BioNLP2024
Please follow these formatting guidelines: https://github.com/acl-org/acl-style-files
Please note that the review process is double-blind.
Final versions of accepted papers will be given one additional page of content (up to 9 pages for long papers, up to 5 pages for short papers) to address reviewers’ comments.
Submissions from ACL rolling review
We will accept ACL rolling review submissions with all reviews and scores. If you are interested in submitting your work for consideration, please contact ddemner at gmail.
INVITED TALK
Speaker: Titipat Achakulvisut, Department of Biomedical Engineering, Mahidol University, Thailand
Biomedical and Data (Bio-Data) lab at Mahidol University
Title: Enhancing Neuroscience Conferences through Natural Language Processing
Abstract: This talk presents the development and implementation of natural language processing (NLP) tools at neuroscience conferences. We have successfully integrated these tools into various conferences, including a recommendation engine at the Society for Neuroscience (SfN) meeting, one-on-one matching at the Conference on Cognitive Computational Neuroscience (CCN), paper-reviewer matching for the Computational and Systems Neuroscience (COSYNE) conference, and reviewer recommendations for NBDT journal. We employ a fine-tuning and contrastive learning approach to adapt transformer-based models, such as MiREAD and SciBERT for neuroscience. We evaluate these models using both distance metrics and recommendation arena assessments. In sum, we explore NLP tools in non-computer science domains, aiming to enhance the interactions of researchers and attendees.
WORKSHOP OVERVIEW AND SCOPE
The BioNLP workshop, associated with the ACL SIGBIOMED special interest group, is an established primary venue for presenting research in language processing and language understanding for the biological and medical domains. The workshop has been running every year since 2002 and continues getting stronger. Many other emerging biomedical and clinical language processing workshops can afford to be more specialized because BioNLP truly encompasses the breadth of the domain and brings together researchers in bio- and clinical NLP from all over the world.
BioNLP 2024 will be particularly interested in transparency of the generative approaches and factuality of the generated text. Language processing that supports DEIA (Diversity, Equity, Inclusion and Accessibility) is still of utmost importance. 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;
- Text simplification;
- Question Answering;
- Resources and strategies for system testing and evaluation;
- Infrastructures and pre-trained language models for biomedical NLP;
- Processing and annotation platforms;
- Synthetic data generation \& data augmentation;
- Translating NLP research into practice;
- Getting reproducible results.
1. Clinical Text generation
- Task 1: Radiology Report Generation
An important medical application of natural language generation (NLG) is to build assistive systems that take X-ray images of a patient and generate a textual report describing clinical observations in the images. This is a clinically important task, offering the potential to reduce radiologists’ repetitive work and generally improve clinical communication. This shared task is using the first large-scale collection of RRG datasets based on MIMIC-CXR, CheXpert, PadChest and CANDID-PTX. Participants will need to generate findings and impression from chest x-rays and will be evaluated on a common leaderboard with recent proposed metrics such as F1-Radgraph and RadCliQ. This shared task aims to benchmark recent progress using common data splits and evaluation implementations.
See details at https://stanford-aimi.github.io/RRG24/
- Task 2: Discharge Me!
The primary objective of this task is to reduce the time and effort clinicians spend on writing detailed notes in the electronic health record (EHR). Clinicians play a crucial role in documenting patient progress in discharge summaries, but the creation of concise yet comprehensive hospital course summaries and discharge instructions often demands a significant amount of time, especially since these sections cannot be readily copied from prior notes. This can lead to clinician burnout and operational inefficiencies within hospital workflows. By streamlining the generation of these sections, we can not only enhance the accuracy and completeness of clinical documentation but also significantly reduce the time clinicians spend on administrative tasks, ultimately improving patient care quality.
See details at https://stanford-aimi.github.io/discharge-me/
2. BioLaySumm
This shared task surrounds the abstractive summarization of biomedical articles, with an emphasis on catering to non-expert audiences through the generation of summaries that are more readable, containing more background information and less technical terminology (i.e., a “lay summary”).
This is the 2nd iteration of BioLaySumm, following the success of the 1st edition of the task at BioNLP 2023 which attracted 56 submissions across 20 different teams. In this edition, we aim to build on last year’s task by introducing a new test set, updating our evaluation protocol, and encouraging participants to explore novel approaches that will help to further advance the state-of-the-art for Lay Summarization.
See details at https://biolaysumm.org/
Organizers
* Dina Demner-Fushman, US National Library of Medicine * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK * Makoto Miwa, Toyota Technological Institute, Japan * Kirk Roberts, UTHealth, Houston, Texas * Jun-ichi Tsujii, National Institute of Advanced Industrial Science and Technology, Japan
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 * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain * Mike Conway, University of Melbourne, Australia * Surabhi Datta, Melax Technologies, USA * Berry de Bruijn, National Research Council, Canada * Dina Demner-Fushman, US National Library of Medicine * Dmitriy Dligach, Loyola University Chicago, USA * Kathleen C. Fraser, National Research Council Canada * Yanjun Gao, University of Wisconsin-Madison, USA * Natalia Grabar, CNRS, U Lille, France * Cyril Grouin, Université Paris-Saclay, CNRS * Tudor Groza, EMBL-EBI * Deepak Gupta, US National Library of Medicine * Thierry Hamon, LIMSI-CNRS, France * Sam Henry, Christopher Newport University, USA * William Hogan, UCSD, USA * Richard Jackson, AstraZeneca * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia * Sarvnaz Karimi, CSIRO, Australia * Nazmul Kazi, University of North Florida, USA * Won Gyu KIM, US National Library of Medicine * Roman Klinger, University of Stuttgart, Germany * Anna Koroleva, Omdena * Andre Lamurias, NOVA School of Science and Technology, Lisbon, Portugal * Majid Latifi, Department of Computer Science, University of York, York, UK * 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 * Diwakar Mahajan, IBM Research, USA * Timothy Miller, Boston Childrens Hospital and Harvard Medical School, USA * Makoto Miwa, Toyota Technological Institute, Japan * Claire Nedellec, French national institute of agronomy (INRA) * Guenter Neumann, DFKI, Germany * Aurélie Névéol, LISN - CNRS, France * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany * Brian Ondov, Yale University, USA * Laura Plaza, Universidad Nacional de Educación a Distancia * Noon Pokaratsiri Goldstein, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) * François Remy, Ghent University, Belgium * Francisco J. Ribadas-Pena, University of Vigo, Spain * Roland Roller, DFKI, Germany * Mourad Sarrouti, CLARA Analytics, USA * 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 * Davy Weissenbacher, Cedars-Sinai, Los Angeles, California, USA * Nathan M. White, James Cook University, Australia * W John Wilbur, US National Library of Medicine * Amelie Wührl, University of Stuttgart, Germany * Dongfang Xu, Cedars-Sinai, USA * Shweta Yadav, University of Illinois Chicago, USA * Jingqing Zhang, Imperial College London, UK * Ayah Zirikly, Johns Hopkins, USA * Pierre Zweigenbaum, LIMSI - CNRS, France