Difference between revisions of "BioNLP 2023"

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*BioNLP 2022 Workshop at ACL, May 26, 2022, Dublin, Ireland
 
*BioNLP 2022 Workshop at ACL, May 26, 2022, Dublin, Ireland
  
   
 
<h2>BioNLP 2022: Program</h2>
 
  
<h4>All times are Ireland timezone (GMT+1)</h4>
+
 
 +
<h2>BioNLP 2022 Program</h2>
 +
 
 +
<h3>All times are Ireland timezone (GMT+1)</h3>
 
   
 
   
  
<table cellspacing="0" cellpadding="2" border="0" valuing="top" width="90%">
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<table cellspacing="0" cellpadding="5" border="0" valuing="top" width="95%">
 
<tr>
 
<tr>
 
<td>09:00–09:10</td><td><b>Opening remarks</b></td>
 
<td>09:00–09:10</td><td><b>Opening remarks</b></td>
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<tr>
 
<tr>
 
<td nowrap valign=top bgcolor=#ededed><b>09:10–10:30</b></td>
 
<td nowrap valign=top bgcolor=#ededed><b>09:10–10:30</b></td>
<td valign=top bgcolor=#ededed>
+
<td valign=top bgcolor=#ededed>
<b>Session 1: Question Answering, Discourse Structure and Clinical Applications (Onsite oral presentations) </b>
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<b>Session 1: Question Answering, Discourse Structure and Clinical Applications (Onsite oral presentations) </b>
</td>
+
</td>
 
</tr>
 
</tr>
 
<tr>
 
<tr>
 
   <td nowrap valign=top>09:10–9:30 </td>
 
   <td nowrap valign=top>09:10–9:30 </td>
 
   <td valign=top><b>Explainable Assessment of Healthcare Articles with QA</b>
 
   <td valign=top><b>Explainable Assessment of Healthcare Articles with QA</b>
   <br> <i>Alodie Boissonnet<sup>1</sup>,&nbsp;Marzieh Saeidi<sup>2</sup>,&nbsp;Vassilis Plachouras<sup>2</sup>,&nbsp;Andreas Vlachos<sup>1</sup></i><br>
+
   <br> <i>Alodie Boissonnet<sup>1</sup>, Marzieh Saeidi<sup>2</sup>, Vassilis Plachouras<sup>2</sup>, Andreas Vlachos<sup>1</sup></i><br>
  <sup>1</sup>University of Cambridge, <sup>2</sup>Facebook
+
<sup>1</sup>University of Cambridge, <sup>2</sup>Facebook
</td>
+
</td>
      </tr>
+
</tr>
 
<tr>
 
<tr>
 
   <td nowrap valign=top>09:30–9:50</td>
 
   <td nowrap valign=top>09:30–9:50</td>
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<br>
 
<br>
 
<i>John Giorgi<sup>1</sup>,&nbsp;Gary Bader<sup>1</sup>,&nbsp;Bo Wang<sup>2</sup></i><br>
 
<i>John Giorgi<sup>1</sup>,&nbsp;Gary Bader<sup>1</sup>,&nbsp;Bo Wang<sup>2</sup></i><br>
  <sup>1</sup>University of Toronto, <sup>2</sup>School of Artificial Intelligence, Jilin University
+
<sup>1</sup>University of Toronto, <sup>2</sup>School of Artificial Intelligence, Jilin University
</td>
+
  </td>
      </tr>
+
</tr>
 +
<tr>
 +
<td nowrap valign=top> 09:50–10:10 </td>
 +
<td valign=top> <b>Position-based Prompting for Health Outcome Generation</b>
 +
<br>
 +
  <i>Micheal Abaho<sup>1</sup>,&nbsp;Danushka Bollegala<sup>2</sup>,&nbsp;Paula Williamson<sup>1</sup>,&nbsp;Susanna Dodd<sup>1</sup></i><br>
 +
  <sup>1</sup>University of Liverpool, <sup>2</sup>University of Liverpool/Amazon
 +
</td>
 +
  </tr>
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> 10:10-10:30</td>
  09:50–10:10
+
<td valign=top>
</td>
+
    <b>How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection</b>
<td valign=top> <b>Position-based Prompting for Health Outcome Generation</b>
+
  <br>
  <br>
+
  <i>Shahla Farzana, Ashwin Deshpande, Natalie Parde</i><br>
  <i>Micheal Abaho<sup>1</sup>,&nbsp;Danushka Bollegala<sup>2</sup>,&nbsp;Paula Williamson<sup>1</sup>,&nbsp;Susanna Dodd<sup>1</sup></i><br>
+
  University of Illinois at Chicago
  <sup>1</sup>University of Liverpool, <sup>2</sup>University of Liverpool/Amazon
+
</td>
</td>
+
</tr>
      </tr>
+
<tr>
    <tr>
+
<td nowrap valign=top bgcolor=#ededed><b>10:30–11:00</b></td>
<td nowrap valign=top>
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<td valign=top bgcolor=#ededed><b><em>Coffee Break</em></b></td>
    10:10-10:30
+
</tr>
</td>
+
<tr>
<td valign=top >
+
<td valign=top bgcolor=#ededed><b>11:00–12:30</b></td>
    <b>How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection</b>
+
<td valign=top bgcolor=#ededed><b>Hybrid Poster Session 1</b></td>
  <br>
+
</tr>
  <i>Shahla Farzana<sup>1</sup>,&nbsp;Ashwin Deshpande<sup>1</sup>,&nbsp;Natalie Parde<sup>2</sup></i><br>
 
  <sup>1</sup>University of Illinois Chicago, <sup>2</sup>University of Illinois at Chicago
 
</td>
 
      </tr>
 
 
 
 
<tr>
 
<tr>
<td nowrap valign=top bgcolor=#ededed>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
<b>10:30–11:00</b>
+
<td>
 +
    <b>Data Augmentation for Biomedical Factoid Question Answering</b>
 +
  <br>
 +
  <em>Dimitris Pappas,  Prodromos Malakasiotis, Ion Androutsopoulos</em><br>
 +
  Athens University of Economics and Business
 
</td>
 
</td>
<td valign=top bgcolor=#ededed>
+
</tr>
<b><em>Coffee Break</em></b>
+
  <tr>
 +
<td nowrap valign=top> &nbsp;&nbsp;</td>
 +
<td>
 +
    <b>Slot Filling for Biomedical Information Extraction</b>
 +
  <br>
 +
  <em>Yannis Papanikolaou, Marlene Staib, Justin Grace, Francine Bennett</em><br>
 +
  Healx Ltd
 
</td>
 
</td>
 
</tr>
 
</tr>
<tr>
+
<tr>
<td valign=top style="padding-top: 14px;">11:00–12:30</td>
+
<td nowrap valign=top>  &nbsp;&nbsp;</td>
<td valign=top style="padding-top: 14px;">
+
<td>
<b>Hybrid Poster Session 1</b>
+
  <b>Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations</b>
 +
  <br>
 +
  <em>Sihang Zeng,&nbsp;Zheng Yuan,&nbsp;Sheng Yu</em><br>
 +
  Tsinghua University
 
</td>
 
</td>
 
</tr>
 
</tr>
  <tr>
 
<td nowrap valign=top>
 
  &nbsp;&nbsp;
 
</td>
 
<td>
 
    <b>Data Augmentation for Biomedical Factoid Question Answering</b>
 
  <br>
 
  <em>Dimitris Pappas<sup>1</sup>,&nbsp;Prodromos Malakasiotis<sup>2</sup>,&nbsp;Ion Androutsopoulos<sup>1</sup></em><br>
 
  <sup>1</sup>Athens University of Economics and Business, <sup>2</sup>Institute of Informatics & Telecommunications, NCSR "Demokritos", Athens University of Economics and Business Informatics Department
 
</td>
 
      </tr>
 
 
<td nowrap valign=top>
 
  &nbsp;&nbsp;
 
</td>
 
<td>
 
    <b>Slot Filling for Biomedical Information Extraction</b>
 
  <br>
 
  <em>Yannis Papanikolaou<sup>1</sup>,&nbsp;Marlene Staib<sup>1</sup>,&nbsp;Justin Grace<sup>2</sup>,&nbsp;Francine Bennett<sup>1</sup></em><br>
 
  <sup>1</sup>Healx Ltd, <sup>2</sup>Healx.ltd
 
</td>
 
      </tr>
 
 
  <tr>
 
<td nowrap valign=top>
 
  &nbsp;&nbsp;
 
</td>
 
<td>
 
    <b>Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations</b>
 
  <br>
 
  <em>Sihang Zeng,&nbsp;Zheng Yuan,&nbsp;Sheng Yu</em><br>
 
  Tsinghua University
 
</td>
 
      </tr>
 
 
 
  <tr>
 
  <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model</b>
<td>
+
  <br>
    <b>BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model</b>
+
  <em>Hongyi Yuan<sup>1</sup>,&nbsp;Zheng Yuan<sup>1</sup>,&nbsp;Ruyi Gan<sup>2</sup>,&nbsp;Jiaxing Zhang<sup>2</sup>,&nbsp;Yutao Xie<sup>2</sup>,&nbsp;Sheng Yu<sup>1</sup></em><br>
  <br>
+
  <sup>1</sup>Tsinghua University, <sup>2</sup>International Digital Economy Academy
  <em>Hongyi Yuan<sup>1</sup>,&nbsp;Zheng Yuan<sup>1</sup>,&nbsp;Ruyi Gan<sup>2</sup>,&nbsp;Jiaxing Zhang<sup>2</sup>,&nbsp;Yutao Xie<sup>2</sup>,&nbsp;Sheng Yu<sup>1</sup></em><br>
+
</td>
  <sup>1</sup>Tsinghua University, <sup>2</sup>International Digital Economy Academy
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>&nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation</b>
<td>
+
  <br>
    <b>Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation</b>
+
  <em>Usman Naseem<sup>1</sup>,&nbsp;Ajay Bandi<sup>2</sup>,&nbsp;Shaina Raza<sup>3</sup>,&nbsp;Junaid Rashid<sup>4</sup>,&nbsp;Bharathi Raja Chakravarthi<sup>5</sup></em><br>
  <br>
+
  <sup>1</sup>University of Sydney, <sup>2</sup>Northwest Missouri State University, USA, <sup>3</sup>University of Toronto, Canada, <sup>4</sup>Kongju National University, South Korea, <sup>5</sup>National University of Ireland Galway
  <em>Usman Naseem<sup>1</sup>,&nbsp;Ajay Bandi<sup>2</sup>,&nbsp;Shaina Raza<sup>3</sup>,&nbsp;Junaid Rashid<sup>4</sup>,&nbsp;Bharathi Raja Chakravarthi<sup>5</sup></em><br>
+
</td>
  <sup>1</sup>University of Sydney, <sup>2</sup>School of Computer Science and Information Systems Northwest Missouri State University Maryville, MO 64468 USA, <sup>3</sup>Health Systems Impact Fellow, University of Toronto  Toronto, Ontario, Canada, <sup>4</sup>Department of Computer Science and Engineering, Kongju National University, South Korea, <sup>5</sup>Data Science Institute, National University of Ireland Galway, Galway, Ireland
 
</td>
 
 
       </tr>
 
       </tr>
 
 
       <tr>
 
       <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>&nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation</b>
<td>
+
  <br>
    <b>Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation</b>
+
  <em>Sixing Yan</em><br>
  <br>
+
  Hong Kong Baptist University
  <em>Sixing Yan</em><br>
+
</td>
  Hong Kong Baptist University
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts</b>
<td>
+
  <br>
    <b>Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts</b>
+
  <em>Uyen Phan<sup>1</sup> and Nhung Nguyen<sup>2</sup></em><br>
  <br>
+
  <sup>1</sup>VNUHCM-University of Science, <sup>2</sup>The University of Manchester
  <em>Uyen Phan<sup>1</sup> and Nhung Nguyen<sup>2</sup></em><br>
+
</td>
  <sup>1</sup>VNUHCM-University of Science, <sup>2</sup>The University of Manchester
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data</b>
<td>
+
<br>
    <b>Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data</b>
+
  <em>Taiki Watanabe<sup>1</sup>,&nbsp;Tomoya Ichikawa<sup>2</sup>,&nbsp;Akihiro Tamura<sup>2</sup>,&nbsp;Tomoya Iwakura<sup>3</sup>,&nbsp;Chunpeng Ma<sup>1</sup>,&nbsp;Tsuneo Kato<sup>2</sup></em><br>
<br>
+
  <sup>1</sup>Fujitsu Ltd., <sup>2</sup>Doshisha University, <sup>3</sup>Fujitsu
  <em>Taiki Watanabe<sup>1</sup>,&nbsp;Tomoya Ichikawa<sup>2</sup>,&nbsp;Akihiro Tamura<sup>2</sup>,&nbsp;Tomoya Iwakura<sup>3</sup>,&nbsp;Chunpeng Ma<sup>1</sup>,&nbsp;Tsuneo Kato<sup>2</sup></em><br>
+
</td>
  <sup>1</sup>Fujitsu Ltd., <sup>2</sup>Doshisha University, <sup>3</sup>Fujitsu
 
</td>
 
 
       </tr>
 
       </tr>
 
 
     <tr>
 
     <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study</b>
<td>
+
  <br>
    <b>SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study</b>
+
  <em>Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu, Xiaopu Zhou, Tze Wing Mak, Yuk Yu Ip, Pascale Fung</em><br>
  <br>
+
  The Hong Kong University of Science and Technology, Hong Kong, China
  <em>Samuel Cahyawijaya<sup>1</sup>,&nbsp;Tiezheng Yu<sup>2</sup>,&nbsp;Zihan Liu<sup>3</sup>,&nbsp;Xiaopu ZHOU<sup>4</sup>,&nbsp;Tze Wing MAK<sup>4</sup>,&nbsp;Yuk Yu IP<sup>4</sup>,&nbsp;Pascale Fung<sup>3</sup></em><br>
+
</td>
  <sup>1</sup>HKUST, <sup>2</sup>The Hong Kong University of Science and Technology, <sup>3</sup>Hong Kong University of Science and Technology, <sup>4</sup>Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>   &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Biomedical NER using Novel Schema and Distant Supervision</b>
<td>
+
  <br>
    <b>Biomedical NER using Novel Schema and Distant Supervision</b>
+
  <em>Anshita Khandelwal,&nbsp;Alok Kar,&nbsp;Veera Chikka,&nbsp;Kamalakar Karlapalem</em><br>
  <br>
+
  International Institute of Information Technology
  <em>Anshita Khandelwal,&nbsp;Alok Kar,&nbsp;Veera Chikka,&nbsp;Kamalakar Karlapalem</em><br>
+
</td>
  International Institute of Information Technology
 
</td>
 
 
       </tr>
 
       </tr>
  
 
     <tr>
 
     <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models</b>
<td>
+
  <br>
    <b>Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models</b>
+
  <em>Naoki Iinuma,&nbsp;Makoto Miwa,&nbsp;Yutaka Sasaki</em><br>
  <br>
+
  Toyota Technological Institute
  <em>Naoki Iinuma,&nbsp;Makoto Miwa,&nbsp;Yutaka Sasaki</em><br>
+
</td>
  Toyota Technological Institute
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Named Entity Recognition for Cancer Immunology Research Using Distant Supervision</b>
<td>
+
  <br>
    <b>Named Entity Recognition for Cancer Immunology Research Using Distant Supervision</b>
+
  <em>Hai-Long Trieu<sup>1</sup>,&nbsp;Makoto Miwa<sup>2</sup>,&nbsp;Sophia Ananiadou<sup>3</sup></em><br>
  <br>
+
  <sup>1</sup>National Institute of Advanced Industrial Science and Technology, <sup>2</sup>Toyota Technological Institute, <sup>3</sup>University of Manchester
  <em>Hai-Long Trieu<sup>1</sup>,&nbsp;Makoto Miwa<sup>2</sup>,&nbsp;Sophia Ananiadou<sup>3</sup></em><br>
+
</td>
  <sup>1</sup>National Institute of Advanced Industrial Science and Technology, <sup>2</sup>Toyota Technological Institute, <sup>3</sup>University of Manchester
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction</b>
<td>
+
  <br>
    <b>Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction</b>
+
  <em>Christian Witte and Philipp Cimiano</em><br>
  <br>
+
  Bielefeld University
  <em>Christian Witte and Philipp Cimiano</em><br>
+
</td>
  Bielefeld University
 
</td>
 
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Pretrained Biomedical Language Models for Clinical NLP in Spanish</b>
<td>
+
  <br>
    <b>Pretrained Biomedical Language Models for Clinical NLP in Spanish</b>
+
  <em>Casimiro Pio Carrino, Joan Llop, Marc Pàmies, Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Joaquín Silveira-Ocampo, Alfonso Valencia, Aitor Gonzalez-Agirre, Marta Villegas</em><br>
  <br>
+
  Barcelona Supercomputing Center
  <em>Casimiro Pio Carrino<sup>1</sup>,&nbsp;Joan Llop<sup>2</sup>,&nbsp;Marc Pàmies<sup>2</sup>,&nbsp;Asier Gutiérrez-Fandiño<sup>2</sup>,&nbsp;Jordi Armengol-Estapé<sup>2</sup>,&nbsp;Joaquín Silveira-Ocampo<sup>1</sup>,&nbsp;Alfonso Valencia<sup>1</sup>,&nbsp;Aitor Gonzalez-Agirre<sup>1</sup>,&nbsp;Marta Villegas<sup>2</sup></em><br>
+
</td>
  <sup>1</sup>Barcelona Supercomputing Center (BSC), <sup>2</sup>Barcelona Supercomputing Center
 
</td>
 
 
       </tr>
 
       </tr>
 
     <tr>
 
     <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
  &nbsp;&nbsp;
+
<td>
</td>
+
    <b>Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training</b>
<td>
+
  <br>
    <b>Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training</b>
+
  <em>Amir Soleimani<sup>1</sup>,&nbsp;Vassilina Nikoulina<sup>2</sup>,&nbsp;Benoit Favre<sup>3</sup>,&nbsp;Salah Ait Mokhtar<sup>2</sup></em><br>
  <br>
+
  <sup>1</sup>University of Amsterdam, <sup>2</sup>Naver Labs Europe, <sup>3</sup>Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
  <em>Amir Soleimani<sup>1</sup>,&nbsp;Vassilina Nikoulina<sup>2</sup>,&nbsp;Benoit Favre<sup>3</sup>,&nbsp;Salah Ait Mokhtar<sup>2</sup></em><br>
+
</td>
  <sup>1</sup>University of Amsterdam, <sup>2</sup>Naver Labs Europe, <sup>3</sup>Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
 
</td>
 
 
       </tr>
 
       </tr>
 
+
    <tr>
    <tr>
+
<td nowrap valign=top>&nbsp;&nbsp;</td>
<td nowrap valign=top>
+
<td>
  &nbsp;&nbsp;
+
    <b>Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts</b>
</td>
+
  <br>
<td>
+
  <em>Saadullah Amin<sup>1</sup>,&nbsp;Noon Pokaratsiri Goldstein<sup>2</sup>,&nbsp;Morgan Wixted<sup>3</sup>,&nbsp;Alejandro Garcia-Rudolph<sup>4</sup>,&nbsp;Catalina Martínez-Costa<sup>5</sup>,&nbsp;Guenter Neumann<sup>1</sup></em><br>
    <b>Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training</b>
+
  <sup>1</sup>DFKI ;amp; Saarland University, <sup>2</sup>DFKI, <sup>3</sup>Saarland University, <sup>4</sup>Institut Guttmann, <sup>5</sup>University of Murcia
  <br>
+
</td>
  <em>Amir Soleimani<sup>1</sup>,&nbsp;Vassilina Nikoulina<sup>2</sup>,&nbsp;Benoit Favre<sup>3</sup>,&nbsp;Salah Ait Mokhtar<sup>2</sup></em><br>
+
  </tr>
  <sup>1</sup>University of Amsterdam, <sup>2</sup>Naver Labs Europe, <sup>3</sup>Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
+
<tr>
</td>
+
<td nowrap valign=top> &nbsp;&nbsp;</td>
      </tr>
+
<td>
 
+
    <b>VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video Classification</b>
    <tr>
+
  <br>
<td nowrap valign=top>
+
  <em>Bin Li<sup>1</sup>,&nbsp;Yixuan Weng<sup>2</sup>,&nbsp;Fei Xia<sup>3</sup>,&nbsp;Bin Sun<sup>1</sup>,&nbsp;Shutao Li<sup>1</sup></em><br>
  &nbsp;&nbsp;
+
  <sup>1</sup>Hunan University, <sup>2</sup>Institute of Automation, Chinese Academy of Sciences, <sup>3</sup>1National Laboratory of Pattern Recognition,Institute of Automation 2University of Chinese Academy of Sciences, Beijing, China
</td>
+
</td>
<td>
 
    <b>VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video Classification</b>
 
  <br>
 
  <em>Bin Li<sup>1</sup>,&nbsp;Yixuan Weng<sup>2</sup>,&nbsp;Fei Xia<sup>3</sup>,&nbsp;Bin Sun<sup>1</sup>,&nbsp;Shutao Li<sup>1</sup></em><br>
 
  <sup>1</sup>Hunan University, <sup>2</sup>Institute of Automation, Chinese Academy of Sciences, <sup>3</sup>1National Laboratory of Pattern Recognition,Institute of Automation 2University of Chinese Academy of Sciences, Beijing, China
 
</td>
 
 
       </tr>
 
       </tr>
  
 
<tr>
 
<tr>
<td valign=top style="padding-top: 14px; bgcolor=#ededed">
+
<td valign=top bgcolor=#ededed>
 
<b>12:30–14:00</b>
 
<b>12:30–14:00</b>
 
</td>
 
</td>
<td valign=top style="padding-top: 14px;">
+
<td valign=top bgcolor=#ededed>
 
<b><em>Lunch Break</em></b>
 
<b><em>Lunch Break</em></b>
 
</td>
 
</td>
 
</tr>
 
</tr>
 
<tr>
 
<tr>
<td valign=top style="padding-top: 14px;">14:00–15:00</td>
+
<td valign=top bgcolor=#ededed>14:00–15:00</td>
<td valign=top style="padding-top: 14px;">
+
<td valign=top bgcolor=#ededed>
 
<b> Summarization and text mining (Onsite oral presentations)  </b>
 
<b> Summarization and text mining (Onsite oral presentations)  </b>
 
</td>
 
</td>
Line 298: Line 242:
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> 14:00-14:20</td>
  14:00-14:20
+
<td>
</td>
+
    <b>GenCompareSum: a hybrid unsupervised summarization method using salience</b>
<td>
+
  <br>
    <b>GenCompareSum: a hybrid unsupervised summarization method using salience</b>
+
  <em>Jennifer Bishop,&nbsp;Qianqian Xie,&nbsp;Sophia Ananiadou</em><br>
  <br>
+
  University of Manchester
  <em>Jennifer Bishop,&nbsp;Qianqian Xie,&nbsp;Sophia Ananiadou</em><br>
+
</td>
  University of Manchester
 
</td>
 
 
       </tr>
 
       </tr>
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>   14:20-14:40</td>
    14:20-14:40
+
<td>
</td>
+
    <b>BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles</b><br>
<td>
+
  <em>Sudipta Singha Roy and Robert E. Mercer </em><br>
    <b>BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles</b>
+
  The University of Western Ontario
  </a><br>
+
</td>
  <em>Sudipta Singha Roy<sup>1</sup> and Robert E. Mercer<sup>2</sup></em><br>
+
</tr>
  <sup>1</sup>University of Western Ontario, <sup>2</sup>The University of Western Ontario</a>
 
</td>
 
      </tr>
 
 
 
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top> 14:40-15:00</td>
  14:40-15:00
+
<td>
</td>
+
    <b>Low Resource Causal Event Detection from Biomedical Literature</b>
<td>
+
  <br>
    <b>Low Resource Causal Event Detection from Biomedical Literature</b>
+
  <em>Zhengzhong Liang, Enrique Noriega-Atala, Clayton Morrison, Mihai Surdeanu</em><br>
  <br>
+
  The University of Arizona
  <em>Zhengzhong Liang<sup>1</sup>,&nbsp;Enrique Noriega-Atala<sup>2</sup>,&nbsp;Clayton Morrison<sup>1</sup>,&nbsp;Mihai Surdeanu<sup>1</sup></em><br>
+
</td>
  <sup>1</sup>University of Arizona, <sup>2</sup>The University of Arizona
 
</td>
 
 
       </tr>
 
       </tr>
  
 
<tr>
 
<tr>
<td valign=top style="padding-top: 14px; bgcolor=#ededed">
+
<td valign=top bgcolor=#ededed><b>15:00–15:30</b></td>
<b>15:00–15:30</b>
+
<td valign=top bgcolor=#ededed>
</td>
 
<td valign=top style="padding-top: 14px;">
 
 
<b><em>Coffee Break</em></b>
 
<b><em>Coffee Break</em></b>
 
</td>
 
</td>
 
</tr>
 
</tr>
 
<tr>
 
<tr>
<td valign=top style="padding-top: 14px;">15:30–17:00</td>
+
<td valign=top bgcolor=#ededed>15:30–17:00</td>
<td valign=top style="padding-top: 14px;">
+
<td valign=top bgcolor=#ededed>
 
<b> Hybrid Poster Session 2 </b>
 
<b> Hybrid Poster Session 2 </b>
 
</td>
 
</td>
 
</tr>
 
</tr>
 
 
     <tr>
 
     <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering</b>
+
    <b>Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering</b>
<br>
+
<br>
  <em>Deepak Gupta and Dina Demner-Fushman</em><br>
+
  <em>Deepak Gupta and Dina Demner-Fushman</em><br>
  National Library of Medicine, NIH
+
  National Library of Medicine, NIH
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations</b>
+
    <b>Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations</b>
  <br>
+
  <br>
  <em>Russell Richie<sup>1</sup>,&nbsp;Sachin Grover<sup>1</sup>,&nbsp;Fuchiang Tsui<sup>2</sup></em><br>
+
  <em>Russell Richie<sup>1</sup>,&nbsp;Sachin Grover<sup>1</sup>,&nbsp;Fuchiang Tsui<sup>2</sup></em><br>
  <sup>1</sup>Children's Hospital of Philadelphia, <sup>2</sup>Children's Hospital of Philadelphia; University of Pennsylvania
+
  <sup>1</sup>Children's Hospital of Philadelphia, <sup>2</sup>Children's Hospital of Philadelphia; University of Pennsylvania
</td>
+
</td>
 
       </tr>
 
       </tr>
  
  
 
  <tr>
 
  <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues</b>
+
    <b>Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues</b>
  <br>
+
  <br>
  <em>Avisha Das<sup>1</sup>,&nbsp;Salih Selek<sup>2</sup>,&nbsp;Alia Warner<sup>2</sup>,&nbsp;Xu Zuo<sup>1</sup>,&nbsp;Yan Hu<sup>1</sup>,&nbsp;Vipina Kuttichi Keloth<sup>1</sup>,&nbsp;Jianfu Li<sup>1</sup>,&nbsp;W. Zheng<sup>1</sup>,&nbsp;Hua Xu<sup>1</sup></em><br>
+
  <em>Avisha Das<sup>1</sup>,&nbsp;Salih Selek<sup>2</sup>,&nbsp;Alia Warner<sup>2</sup>,&nbsp;Xu Zuo<sup>1</sup>,&nbsp;Yan Hu<sup>1</sup>,&nbsp;Vipina Kuttichi Keloth<sup>1</sup>,&nbsp;Jianfu Li<sup>1</sup>,&nbsp;W. Zheng<sup>1</sup>,&nbsp;Hua Xu<sup>1</sup></em><br>
  <sup>1</sup>School of Biomedical Informatics, UTHealth, <sup>2</sup>McGovern Medical School, UTHealth
+
  <sup>1</sup>School of Biomedical Informatics, UTHealth, <sup>2</sup>McGovern Medical School, UTHealth
</td>
+
</td>
 
       </tr>
 
       </tr>
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations</b>
+
    <b>Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations</b>
  <br>
+
  <br>
  <em>Russell Richie<sup>1</sup>,&nbsp;Sachin Grover<sup>1</sup>,&nbsp;Fuchiang Tsui<sup>2</sup></em><br>
+
  <em>Russell Richie<sup>1</sup>,&nbsp;Sachin Grover<sup>1</sup>,&nbsp;Fuchiang Tsui<sup>2</sup></em><br>
  <sup>1</sup>Children's Hospital of Philadelphia, <sup>2</sup>Children's Hospital of Philadelphia; University of Pennsylvania
+
  <sup>1</sup>Children's Hospital of Philadelphia, <sup>2</sup>Children's Hospital of Philadelphia; University of Pennsylvania
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
     <tr>
 
     <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>BanglaBioMed: A Biomedical Named-Entity Annotated Corpus for Bangla (Bengali)</b>
+
    <b>BanglaBioMed: A Biomedical Named-Entity Annotated Corpus for Bangla (Bengali)</b>
  <br>
+
  <br>
  <em>Salim Sazzed</em><br>
+
  <em>Salim Sazzed</em><br>
  Old Dominion University
+
  Old Dominion University
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering</b>
+
    <b>BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering</b>
  <br>
+
  <br>
  <em>Xing David Wang,&nbsp;Ulf Leser,&nbsp;Leon Weber</em><br>
+
  <em>Xing David Wang,&nbsp;Ulf Leser,&nbsp;Leon Weber</em><br>
  Humboldt-Universität zu Berlin
+
  Humboldt-Universität zu Berlin
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection</b>
+
    <b>Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection</b>
  <br>
+
  <br>
  <em>Bosung Kim and Ndapa Nakashole</em><br>
+
  <em>Bosung Kim and Ndapa Nakashole</em><br>
  University of California, San Diego
+
  University of California, San Diego
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
<tr>
 
<tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>ICDBigBird: A Contextual Embedding Model for ICD Code Classification</b>
+
    <b>ICDBigBird: A Contextual Embedding Model for ICD Code Classification</b>
  <br>
+
  <br>
  <em>George Michalopoulos<sup>1</sup>,&nbsp;Michal Malyska<sup>2</sup>,&nbsp;Nicola Sahar<sup>3</sup>,&nbsp;Alexander Wong<sup>1</sup>,&nbsp;Helen Chen<sup>1</sup></em><br>
+
  <em>George Michalopoulos<sup>1</sup>,&nbsp;Michal Malyska<sup>2</sup>,&nbsp;Nicola Sahar<sup>3</sup>,&nbsp;Alexander Wong<sup>1</sup>,&nbsp;Helen Chen<sup>1</sup></em><br>
  <sup>1</sup>University of Waterloo, <sup>2</sup>University of Toronto, <sup>3</sup>Semantic Health
+
  <sup>1</sup>University of Waterloo, <sup>2</sup>University of Toronto, <sup>3</sup>Semantic Health
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
  <tr>
 
  <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation</b>
+
    <b>Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation</b>
  <br>
+
  <br>
  <em>Hillary Ngai<sup>1</sup> and Frank Rudzicz<sup>2</sup></em><br>
+
  <em>Hillary Ngai<sup>1</sup> and Frank Rudzicz<sup>2</sup></em><br>
  <sup>1</sup>Vector Institute for Artificial Intelligence, <sup>2</sup>Vector Institute for Artificial Intelligence, University of Toronto
+
  <sup>1</sup>Vector Institute for Artificial Intelligence, <sup>2</sup>Vector Institute for Artificial Intelligence, University of Toronto
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
  <tr>
 
  <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature</b>
+
    <b>DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature</b>
  <br>
+
  <br>
  <em>Anjani Dhrangadhariya<sup>1</sup> and Henning Müller<sup>2</sup></em><br>
+
  <em>Anjani Dhrangadhariya<sup>1</sup> and Henning Müller<sup>2</sup></em><br>
  <sup>1</sup>HES-SO Valais-Wallis, <sup>2</sup>HES-SO
+
  <sup>1</sup>HES-SO Valais-Wallis, <sup>2</sup>HES-SO
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Improving Romanian BioNER Using a Biologically Inspired System</b>
+
    <b>Improving Romanian BioNER Using a Biologically Inspired System</b>
  <br>
+
  <br>
  <em>Maria Mitrofan<sup>1</sup> and Vasile Pais<sup>2</sup></em><br>
+
  <em>Maria Mitrofan<sup>1</sup> and Vasile Pais<sup>2</sup></em><br>
  <sup>1</sup>RACAI, <sup>2</sup>Research Institute for Artificial Intelligence, Romanian Academy
+
  <sup>1</sup>RACAI, <sup>2</sup>Research Institute for Artificial Intelligence, Romanian Academy
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
  <tr>
 
  <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>EchoGen: Generating Conclusions from Echocardiogram Notes</b>
+
    <b>EchoGen: Generating Conclusions from Echocardiogram Notes</b>
  <br>
+
  <br>
  <em>Liyan Tang<sup>1</sup>,&nbsp;Shravan Kooragayalu<sup>2</sup>,&nbsp;Yanshan Wang<sup>2</sup>,&nbsp;Ying Ding<sup>1</sup>,&nbsp;Greg Durrett<sup>3</sup>,&nbsp;Justin Rousseau<sup>1</sup>,&nbsp;Yifan Peng<sup>4</sup></em><br>
+
  <em>Liyan Tang<sup>1</sup>,&nbsp;Shravan Kooragayalu<sup>2</sup>,&nbsp;Yanshan Wang<sup>2</sup>,&nbsp;Ying Ding<sup>1</sup>,&nbsp;Greg Durrett<sup>3</sup>,&nbsp;Justin Rousseau<sup>1</sup>,&nbsp;Yifan Peng<sup>4</sup></em><br>
  <sup>1</sup>University of Texas at Austin, <sup>2</sup>University of Pittsburgh, <sup>3</sup>UT Austin, <sup>4</sup>Cornell Medicine
+
  <sup>1</sup>University of Texas at Austin, <sup>2</sup>University of Pittsburgh, <sup>3</sup>UT Austin, <sup>4</sup>Cornell Medicine
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
  <tr>
 
  <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record</b>
+
    <b>Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record</b>
  <br>
+
  <br>
  <em>Kevin Xie<sup>1</sup>,&nbsp;Brian Litt<sup>2</sup>,&nbsp;Dan Roth<sup>1</sup>,&nbsp;Colin Ellis<sup>2</sup></em><br>
+
  <em>Kevin Xie<sup>1</sup>,&nbsp;Brian Litt<sup>2</sup>,&nbsp;Dan Roth<sup>1</sup>,&nbsp;Colin Ellis<sup>2</sup></em><br>
  <sup>1</sup>University of Pennsylvania, <sup>2</sup>Perelman School of Medicine, University of Pennsylvania
+
  <sup>1</sup>University of Pennsylvania, <sup>2</sup>Perelman School of Medicine, University of Pennsylvania
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets</b>
+
    <b>Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets</b>
  <br>
+
  <br>
  <em>Mourad Sarrouti,&nbsp;Carson Tao,&nbsp;Yoann Mamy Randriamihaja</em><br>
+
  <em>Mourad Sarrouti,&nbsp;Carson Tao,&nbsp;Yoann Mamy Randriamihaja</em><br>
  Sumitovant Biopharma
+
  Sumitovant Biopharma
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Utility Preservation of Clinical Text After De-Identification</b>
+
    <b>Utility Preservation of Clinical Text After De-Identification</b>
  <br>
+
  <br>
  <em>Thomas Vakili<sup>1</sup> and Hercules Dalianis<sup>2</sup></em><br>
+
  <em>Thomas Vakili<sup>1</sup> and Hercules Dalianis<sup>2</sup></em><br>
  <sup>1</sup>Department of Computer and Systems Sciences, Stockholm University, <sup>2</sup>DSV/Stockholm University
+
  <sup>1</sup>Department of Computer and Systems Sciences, Stockholm University, <sup>2</sup>DSV/Stockholm University
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding</b>
+
    <b>Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding</b>
  <br>
+
  <br>
  <em>Matúš Falis<sup>1</sup>,&nbsp;Hang Dong<sup>2</sup>,&nbsp;Alexandra Birch<sup>1</sup>,&nbsp;Beatrice Alex<sup>1</sup></em><br>
+
  <em>Matúš Falis<sup>1</sup>,&nbsp;Hang Dong<sup>2</sup>,&nbsp;Alexandra Birch<sup>1</sup>,&nbsp;Beatrice Alex<sup>1</sup></em><br>
  <sup>1</sup>The University of Edinburgh, <sup>2</sup>Oxford University
+
  <sup>1</sup>The University of Edinburgh, <sup>2</sup>Oxford University
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Towards Automatic Curation of Antibiotic Resistance Genes via Statement Extraction from Scientific Papers: A Benchmark Dataset and Models</b>
+
    <b>Towards Automatic Curation of Antibiotic Resistance Genes via Statement Extraction from Scientific Papers: A Benchmark Dataset and Models</b>
  <br>
+
  <br>
  <em>Sidhant Chandak<sup>1</sup>,&nbsp;Liqing Zhang<sup>2</sup>,&nbsp;Connor Brown<sup>2</sup>,&nbsp;Lifu Huang<sup>2</sup></em><br>
+
  <em>Sidhant Chandak<sup>1</sup>,&nbsp;Liqing Zhang<sup>2</sup>,&nbsp;Connor Brown<sup>2</sup>,&nbsp;Lifu Huang<sup>2</sup></em><br>
  <sup>1</sup>Indian institute of Technology Kanpur, <sup>2</sup>Virginia Tech
+
  <sup>1</sup>Indian institute of Technology Kanpur, <sup>2</sup>Virginia Tech
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
   <tr>
 
   <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Model Distillation for Faithful Explanations of Medical Code Predictions</b>
+
    <b>Model Distillation for Faithful Explanations of Medical Code Predictions</b>
  <br>
+
  <br>
  <em>Zach Wood-Doughty,&nbsp;Isabel Cachola,&nbsp;Mark Dredze</em><br>
+
  <em>Zach Wood-Doughty,&nbsp;Isabel Cachola,&nbsp;Mark Dredze</em><br>
  Johns Hopkins University
+
  Johns Hopkins University
</td>
+
</td>
 
       </tr>
 
       </tr>
  
 
     <tr>
 
     <tr>
<td nowrap valign=top>
+
<td nowrap valign=top>
  &nbsp;&nbsp;
+
  &nbsp;&nbsp;
</td>
+
</td>
<td>
+
<td>
    <b>Towards Generalizable Methods for Automating Risk Score Calculation</b>
+
    <b>Towards Generalizable Methods for Automating Risk Score Calculation</b>
  <br>
+
  <br>
  <em>Jennifer J Liang<sup>1</sup>,&nbsp;Eric Lehman<sup>2</sup>,&nbsp;Ananya Iyengar<sup>3</sup>,&nbsp;Diwakar Mahajan<sup>1</sup>,&nbsp;Preethi Raghavan<sup>1</sup>,&nbsp;Cindy Y. Chang<sup>4</sup>,&nbsp;Peter Szolovits<sup>2</sup></em><br>
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  <em>Jennifer J Liang<sup>1</sup>,&nbsp;Eric Lehman<sup>2</sup>,&nbsp;Ananya Iyengar<sup>3</sup>,&nbsp;Diwakar Mahajan<sup>1</sup>,&nbsp;Preethi Raghavan<sup>1</sup>,&nbsp;Cindy Y. Chang<sup>4</sup>,&nbsp;Peter Szolovits<sup>2</sup></em><br>
  <sup>1</sup>IBM Research, <sup>2</sup>MIT, <sup>3</sup>Northeastern University, <sup>4</sup>Brigham and Women's Hospital
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  <sup>1</sup>IBM Research, <sup>2</sup>MIT, <sup>3</sup>Northeastern University, <sup>4</sup>Brigham and Women's Hospital
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    <b>DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem</b>
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    <b>DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem</b>
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  <em>Wojciech Kusa<sup>1</sup>,&nbsp;Georgios Peikos<sup>2</sup>,&nbsp;Óscar Espitia<sup>3</sup>,&nbsp;Allan Hanbury<sup>1</sup>,&nbsp;Gabriella Pasi<sup>4</sup></em><br>
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  <em>Wojciech Kusa<sup>1</sup>,&nbsp;Georgios Peikos<sup>2</sup>,&nbsp;Óscar Espitia<sup>3</sup>,&nbsp;Allan Hanbury<sup>1</sup>,&nbsp;Gabriella Pasi<sup>4</sup></em><br>
  <sup>1</sup>TU Wien, <sup>2</sup>University of Milano-Bicocca, <sup>3</sup>University of Milano Bicocca, <sup>4</sup>Università degli Studi di Milano Bicocca
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  <sup>1</sup>TU Wien, <sup>2</sup>University of Milano-Bicocca, <sup>3</sup>University of Milano Bicocca, <sup>4</sup>Università degli Studi di Milano Bicocca
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Revision as of 08:09, 17 April 2022

SIGBIOMED

BIONLP 2022 @ ACL 2022

The 21st BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with ACL 2022

IMPORTANT DATES

  • March 7, 2022: Workshop Paper Due Date
  • Submission site: https://www.softconf.com/acl2022/BioNLP2022
  • March 28, 2022: Notification of Acceptance
  • April 10, 2022: Camera-ready papers due
  • BioNLP 2022 Workshop at ACL, May 26, 2022, Dublin, Ireland


BioNLP 2022 Program

All times are Ireland timezone (GMT+1)


09:00–09:10Opening remarks
09:10–10:30
Session 1: Question Answering, Discourse Structure and Clinical Applications (Onsite oral  presentations) 
09:10–9:30 Explainable Assessment of Healthcare Articles with QA
 
Alodie Boissonnet1, Marzieh Saeidi2, Vassilis Plachouras2, Andreas Vlachos1
1University of Cambridge, 2Facebook
09:30–9:50 A sequence-to-sequence approach for document-level relation extraction


John Giorgi1, Gary Bader1, Bo Wang2

1University of Toronto, 2School of Artificial Intelligence, Jilin University
09:50–10:10 Position-based Prompting for Health Outcome Generation

Micheal Abaho1, Danushka Bollegala2, Paula Williamson1, Susanna Dodd1
1University of Liverpool, 2University of Liverpool/Amazon
10:10-10:30
   How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection
 
Shahla Farzana, Ashwin Deshpande, Natalie Parde
University of Illinois at Chicago
10:30–11:00 Coffee Break
11:00–12:30 Hybrid Poster Session 1
  
   Data Augmentation for Biomedical Factoid Question Answering
 
Dimitris Pappas, Prodromos Malakasiotis, Ion Androutsopoulos
Athens University of Economics and Business
  
   Slot Filling for Biomedical Information Extraction
 
Yannis Papanikolaou, Marlene Staib, Justin Grace, Francine Bennett
Healx Ltd
  
 Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations
 
Sihang Zeng, Zheng Yuan, Sheng Yu
Tsinghua University
  
   BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
 
Hongyi Yuan1, Zheng Yuan1, Ruyi Gan2, Jiaxing Zhang2, Yutao Xie2, Sheng Yu1
1Tsinghua University, 2International Digital Economy Academy
  
   Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation
 
Usman Naseem1, Ajay Bandi2, Shaina Raza3, Junaid Rashid4, Bharathi Raja Chakravarthi5
1University of Sydney, 2Northwest Missouri State University, USA, 3University of Toronto, Canada, 4Kongju National University, South Korea, 5National University of Ireland Galway
  
   Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation
 
Sixing Yan
Hong Kong Baptist University
  
   Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts
 
Uyen Phan1 and Nhung Nguyen2
1VNUHCM-University of Science, 2The University of Manchester
  
   Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data

Taiki Watanabe1, Tomoya Ichikawa2, Akihiro Tamura2, Tomoya Iwakura3, Chunpeng Ma1, Tsuneo Kato2
1Fujitsu Ltd., 2Doshisha University, 3Fujitsu
  
   SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study
 
Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu, Xiaopu Zhou, Tze Wing Mak, Yuk Yu Ip, Pascale Fung
The Hong Kong University of Science and Technology, Hong Kong, China
  
   Biomedical NER using Novel Schema and Distant Supervision
 
Anshita Khandelwal, Alok Kar, Veera Chikka, Kamalakar Karlapalem
International Institute of Information Technology
  
   Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models
 
Naoki Iinuma, Makoto Miwa, Yutaka Sasaki
Toyota Technological Institute
  
   Named Entity Recognition for Cancer Immunology Research Using Distant Supervision
 
Hai-Long Trieu1, Makoto Miwa2, Sophia Ananiadou3
1National Institute of Advanced Industrial Science and Technology, 2Toyota Technological Institute, 3University of Manchester
  
   Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction
 
Christian Witte and Philipp Cimiano
Bielefeld University
  
   Pretrained Biomedical Language Models for Clinical NLP in Spanish
 
Casimiro Pio Carrino, Joan Llop, Marc Pàmies, Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Joaquín Silveira-Ocampo, Alfonso Valencia, Aitor Gonzalez-Agirre, Marta Villegas
Barcelona Supercomputing Center
  
   Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training
 
Amir Soleimani1, Vassilina Nikoulina2, Benoit Favre3, Salah Ait Mokhtar2
1University of Amsterdam, 2Naver Labs Europe, 3Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
  
   Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts
 
Saadullah Amin1, Noon Pokaratsiri Goldstein2, Morgan Wixted3, Alejandro Garcia-Rudolph4, Catalina Martínez-Costa5, Guenter Neumann1
1DFKI ;amp; Saarland University, 2DFKI, 3Saarland University, 4Institut Guttmann, 5University of Murcia
  
   VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video Classification
 
Bin Li1, Yixuan Weng2, Fei Xia3, Bin Sun1, Shutao Li1
1Hunan University, 2Institute of Automation, Chinese Academy of Sciences, 31National Laboratory of Pattern Recognition,Institute of Automation 2University of Chinese Academy of Sciences, Beijing, China

12:30–14:00

Lunch Break

14:00–15:00

Summarization and text mining (Onsite oral presentations)

14:00-14:20
   GenCompareSum: a hybrid unsupervised summarization method using salience
 
Jennifer Bishop, Qianqian Xie, Sophia Ananiadou
University of Manchester
14:20-14:40
   BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles
Sudipta Singha Roy and Robert E. Mercer
The University of Western Ontario
14:40-15:00
   Low Resource Causal Event Detection from Biomedical Literature
 
Zhengzhong Liang, Enrique Noriega-Atala, Clayton Morrison, Mihai Surdeanu
The University of Arizona
15:00–15:30

Coffee Break

15:30–17:00

Hybrid Poster Session 2

    
   Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering

Deepak Gupta and Dina Demner-Fushman
National Library of Medicine, NIH
    
   Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations
 
Russell Richie1, Sachin Grover1, Fuchiang Tsui2
1Children's Hospital of Philadelphia, 2Children's Hospital of Philadelphia; University of Pennsylvania
    
   Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues
 
Avisha Das1, Salih Selek2, Alia Warner2, Xu Zuo1, Yan Hu1, Vipina Kuttichi Keloth1, Jianfu Li1, W. Zheng1, Hua Xu1
1School of Biomedical Informatics, UTHealth, 2McGovern Medical School, UTHealth
    
   Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations
 
Russell Richie1, Sachin Grover1, Fuchiang Tsui2
1Children's Hospital of Philadelphia, 2Children's Hospital of Philadelphia; University of Pennsylvania
    
   BanglaBioMed: A Biomedical Named-Entity Annotated Corpus for Bangla (Bengali)
 
Salim Sazzed
Old Dominion University
    
   BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering
 
Xing David Wang, Ulf Leser, Leon Weber
Humboldt-Universität zu Berlin
    
   Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection
 
Bosung Kim and Ndapa Nakashole
University of California, San Diego
    
   ICDBigBird: A Contextual Embedding Model for ICD Code Classification
 
George Michalopoulos1, Michal Malyska2, Nicola Sahar3, Alexander Wong1, Helen Chen1
1University of Waterloo, 2University of Toronto, 3Semantic Health
    
   Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation
 
Hillary Ngai1 and Frank Rudzicz2
1Vector Institute for Artificial Intelligence, 2Vector Institute for Artificial Intelligence, University of Toronto
    
   DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature
 
Anjani Dhrangadhariya1 and Henning Müller2
1HES-SO Valais-Wallis, 2HES-SO
    
   Improving Romanian BioNER Using a Biologically Inspired System
 
Maria Mitrofan1 and Vasile Pais2
1RACAI, 2Research Institute for Artificial Intelligence, Romanian Academy
    
   EchoGen: Generating Conclusions from Echocardiogram Notes
 
Liyan Tang1, Shravan Kooragayalu2, Yanshan Wang2, Ying Ding1, Greg Durrett3, Justin Rousseau1, Yifan Peng4
1University of Texas at Austin, 2University of Pittsburgh, 3UT Austin, 4Cornell Medicine
    
   Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record
 
Kevin Xie1, Brian Litt2, Dan Roth1, Colin Ellis2
1University of Pennsylvania, 2Perelman School of Medicine, University of Pennsylvania
    
   Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets
 
Mourad Sarrouti, Carson Tao, Yoann Mamy Randriamihaja
Sumitovant Biopharma
    
   Utility Preservation of Clinical Text After De-Identification
 
Thomas Vakili1 and Hercules Dalianis2
1Department of Computer and Systems Sciences, Stockholm University, 2DSV/Stockholm University
    
   Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding
 
Matúš Falis1, Hang Dong2, Alexandra Birch1, Beatrice Alex1
1The University of Edinburgh, 2Oxford University
    
   Towards Automatic Curation of Antibiotic Resistance Genes via Statement Extraction from Scientific Papers: A Benchmark Dataset and Models
 
Sidhant Chandak1, Liqing Zhang2, Connor Brown2, Lifu Huang2
1Indian institute of Technology Kanpur, 2Virginia Tech
    
   Model Distillation for Faithful Explanations of Medical Code Predictions
 
Zach Wood-Doughty, Isabel Cachola, Mark Dredze
Johns Hopkins University
    
   Towards Generalizable Methods for Automating Risk Score Calculation
 
Jennifer J Liang1, Eric Lehman2, Ananya Iyengar3, Diwakar Mahajan1, Preethi Raghavan1, Cindy Y. Chang4, Peter Szolovits2
1IBM Research, 2MIT, 3Northeastern University, 4Brigham and Women's Hospital
    
   DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem
 
Wojciech Kusa1, Georgios Peikos2, Óscar Espitia3, Allan Hanbury1, Gabriella Pasi4
1TU Wien, 2University of Milano-Bicocca, 3University of Milano Bicocca, 4Università degli Studi di Milano Bicocca

Submission Types & Requirements

Following the previous conferences, BioNLP 2022 will be open for two types of submissions: long and short papers. Please follow ACL guidelines https://acl-org.github.io/ACLPUB/formatting.html and templates: https://github.com/acl-org/acl-style-files

Overleaf templates: https://www.overleaf.com/project/5f64f1fb97c4c50001b60549

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. Despite, or maybe due to reaching maturity, the field of Biomedical NLP continues getting stronger. BioNLP welcomes and encourages 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.

BioNLP 2022 will be particularly interested in work on detection and mitigation of bias, BioNLP research in languages other than English, particularly, under-represented languages, and health disparities.

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

  • 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;
  • Translating NLP research into practice;
  • Getting reproducible results.

Program Committee

 * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK 
 * Saadullah Amin, Saarland University, Germany
 * Emilia Apostolova, Anthem, Inc., USA
 * Eiji Aramaki, University of Tokyo, Japan 
 * Timothy Baldwin, University of Melbourne, Australia
 * Spandana Balumuri, National Institute of Technology Karnataka, India
 * Steven Bethard, University of Arizona, USA
 * Robert Bossy, Inrae, Université Paris Saclay, France
 * Berry de Bruijn, National Research Council Canada 
 * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain
 * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA 
 * Fenia Christopoulou, Huawei Noah's Ark lab, UK
 * Brian Connolly, Ohio, USA
 * Mike Conway, University of Utah, USA
 * Manirupa Das, Amazon, USA
 * Surabhi Datta, The University of Texas Health Science Center at Houston, USA 
 * Dina Demner-Fushman, US National Library of Medicine 
 * Dmitriy Dligach,  Loyola University Chicago, USA
 * Kathleen C. Fraser,  National Research Council Canada
 * Travis Goodwin, US National Library of Medicine 
 * Natalia Grabar, CNRS, U Lille, France
 * Cyril Grouin, LIMSI - CNRS, France 
 * Tudor Groza, EMBL-EBI
 * Deepak Gupta, US National Library of Medicine 
 * Sam Henry, Christopher Newport University, USA
 * William Hogan, UCSD, USA
 * Kexin Huang, Stanford University, USA
 * Brian Hur, University of Melbourne, Australia
 * Richard Jackson, AstraZeneca
 * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 * Sarvnaz Karimi, CSIRO, Australia
 * Nazmul Kazi,  Montana State University, USA
 * Won Gyu KIM, US National Library of Medicine 
 * Ari Klein, University of Pennsylvania, USA
 * Roman Klinger, University of Stuttgart, Germany
 * Andre Lamurias, Aalborg University, DK
 * Majid Latifi, National College of Ireland 
 * 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 Thomas J. Watson Research Center, USA
 * Mark-Christoph Müller, Heidelberg Institute for Theoretical Studies, Germany
 * Claire Nédellec, INRA, Université Paris-Saclay, FR
 * Guenter Neumann, DFKI, Saarland, Germany
 * Aurelie Neveol, LIMSI - CNRS, France 
 * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany
 * Yifan Peng,  Weill Cornell Medical College, USA
 * Francisco J. Ribadas-Pena, Universidade de Vigo, Spain
 * Anthony Rios, The University of Texas at San Antonio, USA
 * Angus Roberts, King's College London, UK 
 * Kirk Roberts, The University of Texas Health Science Center at Houston, USA 
 * Roland Roller, DFKI, Germany
 * Mourad Sarrouti, Sumitovant Biopharma, Inc., USA
 * Mario Sänger, Humboldt-Universität zu Berlin, Germany 
 * Diana Sousa, Universidade de Lisboa, Portugal
 * Michael Spranger, Sony, Tokyo, Japan
 * Peng Su, University of Delaware, USA
 * Madhumita Sushil, University of California, San Francisco, USA
 * Karin Verspoor, RMIT University, Melbourne, Australia 
 * Roger Wattenhofer, ETH Zurich, Switzerland
 * Leon Weber, Humboldt Universität Berlin, Germany
 * Nathan M. White, James Cook University, Australia
 * Davy Weissenbacher, University of Pennsylvania, USA
 * W John Wilbur, US National Library of Medicine 
 * Amelie Wührl,  University of Stuttgart, Germany
 * Dongfang Xu, Harvard University, USA
 * Shweta Yadav, University of Illinois Chicago, USA
 * Jingqing Zhang,  Imperial College London, UK
 * Ayah Zirikly, Johns Hopkins University, USA
 * Pierre Zweigenbaum, LIMSI - CNRS, France

SHARED TASK: MedVidQA 2022

The first challenge on Medical Video Question Answering is collocated with the BioNLP 2022 Workshop. MedVidQA focuses on providing relevant segments of videos as answers to health-related questions. Medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions. Please check the challenge website for details on the tasks, datasets, and submission guidelines: https://medvidqa.github.io


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 


Dual submission policy

Papers may NOT be submitted to the BioNLP 2022 workshop if they are or will be concurrently submitted to another meeting or publication.