Difference between revisions of "BioNLP 2023"

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[[SIGBIOMED]]
 
[[SIGBIOMED]]
  
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<font size="4"><b>BIONLP 2023 and Shared Tasks @ ACL 2023</b></font>
  
<font size="4"><b>BIONLP 2020</b></font>
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The 22nd BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with [https://2023.aclweb.org/ ACL 2023]
<font size="3">Seattle, Washington, July 9, 2020  -- Online</font>
 
  
An ACL 2020 Workshop associated with the SIGBIOMED special interest group
 
  
===IMPORTANT DATES ===
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===IMPORTANT DATES===
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* April 24, 2023: Workshop Paper Due Date.
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* Submission site for the workshop only: https://softconf.com/acl2023/BioNLP2023/
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* Submission site for the SHARED TASKS only:  https://softconf.com/acl2023/BioNLP2023-ST
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<!-- *Submission site: https://www.softconf.com/acl2022/BioNLP2022 -->
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* May 29, 20232: Notification of Acceptance
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* June 6, 2023: Camera-ready papers due
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* June 12, 2023: Pre-recorded video due
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 +
Video is optional. Instructions (below) are for the video only, not for the final paper submission. Video should not exceed 10 minutes.
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 +
Instructions:
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  https://docs.google.com/presentation/d/1STKSZ22v3ucS9smfDfhREQhwRB9_bIwu7mnVYKUq7A8/edit?usp=sharing
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 +
Form (linked in SLIDE 4)
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https://acl2023workshops.paperform.co/
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* <b>BioNLP 2023</b> Workshop at ACL, July 13, 2023, Toronto, Canada
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Registration: https://2023.aclweb.org/registration/
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===VISA Information===
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ACL organizers are processing the requests.
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Please see the instructions here: https://2023.aclweb.org/blog/visa-info/
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===Poster size: ===
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All posters should be A0, orientation: Portrait.
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===BioNLP 2023: Program===
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<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>
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<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>
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 +
<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>
  
*Submission deadline: <del>Friday, March 20,  2020 </del> '''New: Friday, April, 3,  2020,''' 11:59 PM Eastern US
+
  <tr><td valign=top width=100>'''17:45-18:00'''</td> <td><b>Closing remarks</b></td></tr>
https://www.softconf.com/acl2020/BioNLP2020/
 
*Notification of acceptance:  <del>Friday, April 24, 2020 </del> '''New:''' Tuesday, April 28, 2020
 
*Camera-ready copy due from authors:  <del>'Sunday, May 3, 2020 </del> '''New:''' Wednesday, May 6, 2020
 
*'''Workshop: July 9, 2020'''
 
  
===Program Committee===
+
</table>
 +
 
 +
===BioNLP 2023 Invited Talk===
 +
 
 +
Title: Dementia Detection from Speech: New Developments and Future Directions
 +
  
  <!-- * Hadi Amiri, Harvard Medical School, USA -->
+
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.
  * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
+
  * Emilia Apostolova, Language.ai, USA
+
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.
  * Eiji Aramaki, University of Tokyo, Japan
+
   
  * Asma Ben Abacha, US National Library of Medicine 
 
<!--  * Cosmin (Adi) Bejan, Vanderbilt University, Nashville, TN -->
 
  * Siamak Barzegar, Barcelona Supercomputing Center, Spain  <!-- check 2021-->
 
<!--  * Sai Bhaskar, Carnegie Mellon University, Pittsburgh, PA -->
 
  * Olivier Bodenreider, US National Library of Medicine
 
  * Leonardo Campillos Llanos, Universidad Autónoma de Madrid, Spain
 
  <!-- * Juan Miguel Cejuela, tagtog, Munich, Bavaria, Germany -->
 
  * Qingyu Chen, US National Library of Medicine  <!-- check 2021-->
 
  * Fenia Christopoulou, National Centre for Text Mining and University of Manchester, UK
 
  * Aaron Cohen, Oregon Health & Science University, USA
 
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA
 
  * Brian Connolly, Kroger Digital, USA
 
  * Viviana Cotik, University of Buenos Aires, Argentina <!-- check 2021-->
 
  * Manirupa Das, Amazon Search, Seattle, WA, USA
 
  * Dina Demner-Fushman, US National Library of Medicine
 
  * Bart Desmet, Clinical Center, National Institutes of Health, USA
 
  * Travis Goodwin, The University of Texas at Dallas, USA
 
  * Natalia Grabar, CNRS, France
 
  * Cyril Grouin, LIMSI - CNRS, France
 
  * Tudor Groza, The Garvan Institute of Medical Research, Australia
 
  * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
 
  * Halil Kilicoglu, University of Illinois at Urbana-Champaign, USA
 
  * Ari Klein, University of Pennsylvania, USA
 
<!-- * Zfania Tom Korach, Harvard Medical School, USA -->
 
  * André Lamúrias, University of Lisbon, Portugal
 
  * Majid Latifi, Trinity College Dublin, Ireland
 
  * Alberto Lavelli, FBK-ICT, Italy
 
  * Robert Leaman, US National Library of Medicine
 
  * Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany
 
  <!-- * Gal Levy-Fix, Columbia University, NY -->
 
  * Maolin Li, National Centre for Text Mining and University of Manchester, UK
 
  * Zhiyong Lu, US National Library of Medicine
 
  <!-- * Ramon Maldonado, The University of Texas at Dallas, USA -->
 
  * Timothy Miller, Children’s Hospital Boston, USA
 
  <!-- * Roser Morante, VU University Amsterdam,  Netherlands -->
 
<!--  * 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, German Federal Institute for Risk Assessment, Germany
 
  * Denis Newman-Griffis, Clinical Center, National Institutes of Health, USA
 
  * Nhung Nguyen, The University of Manchester, UK
 
  * Karen O'Connor, University of Pennsylvania, USA
 
  * Naoaki Okazaki, Tokyo Institute of Technology, Japan
 
  * Yifan Peng, US National Library of Medicine
 
  * Laura Plaza, UNED, Madrid, Spain
 
<!-- * Sampo Pyysalo, University of Cambridge, UK -->
 
<!--  * Alastair Rae, US National Library of Medicine -->
 
<!--  * Bastien Rance, European Hospital Georges Pompidou, France -->
 
  * Francisco J. Ribadas-Pena, University of Vigo, Spain
 
  <!-- * Fabio Rinaldi,  University of Zurich, Switzerland  -->
 
  * Angus Roberts, The University of Sheffield, UK
 
  * Kirk Roberts, The University of Texas Health Science Center at Houston, USA
 
  * Roland Roller, DFKI GmbH, Berlin, Germany
 
<!--  * Sumegh Roychowdhury, Indian Institute of Technology Kharagpur -->
 
<!-- * Rashi Rungta, Carnegie Mellon University, Pittsburgh, PA -->
 
<!--  * Max Savery, US National Library of Medicine -->
 
  <!-- * Prakhar Sharma, Indian Institute of Technology, Kharagpur -->
 
  <!-- * Chaitanya Shivade, IBM Research, Almaden, USA -->
 
  * Diana Sousa, University of Lisbon, Portugal
 
  <!-- * Noha Seddik Tawfik, Arab Academy for Science and Technology, Egypt -->
 
<!--  * Thy Thy Tran, National Centre for Text Mining and University of Manchester, UK -->
 
<!--  * Sumithra Velupillai, King’s College London, UK --?
 
  <!-- * Byron C. Wallace,  University of Texas at Austin, USA -->
 
  * Karin Verspoor, The University of Melbourne, Australia
 
  * Davy Weissenbacher, University of Pennsylvania, USA
 
  * W John Wilbur, US National Library of Medicine
 
  * Shankai Yan, US National Library of Medicine
 
  <!-- * Amir Yazdavar, Wright State University, USA -->
 
  * Chrysoula Zerva, National Centre for Text Mining and University of Manchester, UK
 
  * Ayah Zirikly, Clinical Center, National Institutes of Health, USA
 
<!-- * Seyedjamal Zolhavarieh, The University of Auckland, NZ -->
 
  * 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 and University of Manchester, UK
 
  
 
===WORKSHOP OVERVIEW AND SCOPE===
 
===WORKSHOP OVERVIEW AND SCOPE===
  
The ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in  
+
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.
language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP  
+
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.
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 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.
The workshop will continue presenting work on a broad and interesting range of topics in NLP.  
+
 
 +
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.
  
The 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
 
* 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
 
* Explainable models for biomedical NLP
 
* Multi-modal models for biomedical NLP
 
* Getting reproducible results
 
* BioNLP research in languages other than English
 
  
 
===SUBMISSION INSTRUCTIONS===
 
===SUBMISSION INSTRUCTIONS===
  
Two types of submissions are invited: full papers and short papers.  
+
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 papers should not exceed eight (8) pages of text, plus unlimited references.  
+
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.  
 
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.  
 
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.  
 
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.   
 
This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation.   
Line 140: Line 304:
 
Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
 
Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
  
Please see https://acl2020.org/calls/papers/ for templates.
 
  
 +
====Electronic Submission====
 +
Submissions must be electronic and in PDF format, using the Softconf START conference management system at https://softconf.com/acl2023/BioNLP2023/
  
<!-- to open later
+
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/
====Electronic Submission====
 
Submissions must be electronic and in PDF format, using the Softconf START conference management system at    https://www.softconf.com/acl2019/bionlp/
 
We strongly recommend consulting the ACL 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. Please see information about paper formatting requirements and style  at http://www.acl2019.org/EN/call-for-papers.xhtml. Scroll down to “Paper Submission and Templates.”
 
  
 
<b>Submissions need to be anonymous.</b>
 
<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.
 
 
====Dual submission policy====
 
Papers may '''NOT''' be submitted to the BioNLP 2020 workshop if they are or will be concurrently submitted to another meeting or publication.
 
 
 
<!-- 2019
 
 
 
<font size="4"><b>BIONLP 2019</b></font>
 
<br/>
 
<font size="3">Florence, Italy, Thursday, August 1, 2019</font>
 
 
 
An ACL 2019 Workshop associated with the SIGBIOMED special interest group and featuring an associated task: MEDIQA 2019 ( https://sites.google.com/view/mediqa2019)
 
 
 
 
 
===IMPORTANT DATES ===
 
 
 
*Submission deadline: Friday May 10, 2019 11:59 PM Eastern US
 
*Notification of acceptance: Friday, May 31, 2019
 
*Camera-ready copy due from authors: '''Friday, June 7''', 2019 -- '''Firm deadline due to ACL schedule'''.
 
*'''Workshop: Thursday, August 1, 2019'''
 
 
 
 
 
<h2>BioNLP 2019 WORKSHOP PROGRAM</h2>
 
 
 
<table cellspacing="0" cellpadding="5" border="0"><tr><td colspan=2 style="padding-top: 14px;"><b>Thursday August 1, 2019</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: Clinical and Translational NLP</b></td></tr>
 
<tr><td valign=top width=100>8:45&#8211;9:00</td><td valign=top align=left><i>Classifying the reported ability in clinical mobility descriptions</i><br>
 
Denis Newman-Griffis, Ayah Zirikly, Guy Divita, Bart Desmet</td></tr>
 
<tr><td valign=top width=100>9:00&#8211;9:15</td><td valign=top align=left><i>Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes </i><br>
 
Steven Kester Yuwono, Hwee Tou Ng, Kee Yuan Ngiam</td></tr>
 
<tr><td valign=top width=100>9:15&#8211;9:30</td><td valign=top align=left><i>A Paraphrase Generation System for EHR Question Answering</i><br>
 
Sarvesh Soni and Kirk Roberts</td></tr>
 
<tr><td valign=top width=100>9:30&#8211;9:45</td><td valign=top align=left><i>REflex: Flexible Framework for Relation Extraction in Multiple Domains</i><br>
 
Geeticka Chauhan, Matthew McDermott, Peter Szolovits</td></tr>
 
<tr><td valign=top width=100>9:45&#8211;10:00</td><td valign=top align=left><i>Analysing Representations of Memory Impairment in a Clinical Notes Classification Model</i><br>
 
Mark Ormerod, Jesús Martínez-del-Rincón, Neil Robertson, Bernadette McGuinness, Barry Devereux</td></tr>
 
<tr><td valign=top width=100>10:00&#8211;10:15</td><td valign=top align=left><i>Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets</i><br>
 
Yifan Peng, Shankai Yan, Zhiyong Lu</td></tr>
 
<tr><td valign=top width=100>10:15&#8211;10:30</td><td valign=top align=left><i>Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support</i><br>
 
Emilia Apostolova, Tony Wang, Tim Tschampel, Ioannis Koutroulis, Tom Velez</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;12:00</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>MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language</i><br>
 
Maria Mitrofan, Verginica Barbu Mititelu, Grigorina Mitrofan</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Domain Adaptation of SRL Systems for Biological Processes</i><br>
 
Dheeraj Rajagopal, Nidhi Vyas, Aditya Siddhant, Anirudha Rayasam, Niket Tandon, Eduard Hovy</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Deep Contextualized Biomedical Abbreviation Expansion</i><br>
 
Qiao Jin, Jinling Liu, Xinghua Lu</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>RNN Embeddings for Identifying Difficult to Understand Medical Words</i><br>
 
Hanna Pylieva, Artem Chernodub, Natalia Grabar, Thierry Hamon</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>A distantly supervised dataset for automated data extraction from diagnostic studies</i><br>
 
Christopher Norman, Mariska Leeflang, René Spijker, Evangelos Kanoulas, Aurélie Névéol</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Query selection methods for automated corpora construction with a use case in food-drug interactions</i><br>
 
Georgeta Bordea, Tsanta Randriatsitohaina, Fleur Mougin, Natalia Grabar, Thierry Hamon</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Enhancing biomedical word embeddings by retrofitting to verb clusters </i><br>
 
Billy Chiu, Simon Baker, Martha Palmer, Anna Korhonen</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics</i><br>
 
Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns</i><br>
 
Julien Fauqueur, Ashok Thillaisundaram, Theodosia Togia</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>First Steps towards Building a Medical Lexicon for Spanish with Linguistic and Semantic Information</i><br>
 
Leonardo Campillos-Llanos</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing</i><br>
 
Xindi Wang and Robert E. Mercer</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>BioRelEx 1.0: Biological Relation Extraction Benchmark </i><br>
 
Hrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, Aram Galstyan</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Extraction of Lactation Frames from Drug Labels and LactMed</i><br>
 
Heath Goodrum, Meghana Gudala, Ankita Misra, Kirk Roberts</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions</i><br>
 
Natalia Viani, Hegler Tissot, Ariane Bernardino, Sumithra Velupillai</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms</i><br>
 
Leonie Grön, Ann Bertels, Kris Heylen</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding</i><br>
 
Hichem Mezaoui, Isuru Gunasekara, Aleksandr Gontcharov</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Contributions to Clinical Named Entity Recognition in Portuguese</i><br>
 
Fábio Lopes, César Teixeira, Hugo Gonçalo Oliveira</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?</i><br>
 
Zhaodong Yan, Serena Jeblee, Graeme Hirst</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Is artificial data useful for biomedical Natural Language Processing algorithms?</i><br>
 
Zixu Wang, Julia Ive, Sumithra Velupillai, Lucia Specia</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>ChiMed: A Chinese Medical Corpus for Question Answering</i><br>
 
Yuanhe Tian, Weicheng Ma, Fei Xia, Yan Song</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Clinical Concept Extraction for Document-Level Coding</i><br>
 
Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, Jacob Eisenstein</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Clinical Case Reports for NLP</i><br>
 
Cyril Grouin, Natalia Grabar, Vincent Claveau, Thierry Hamon</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Two-stage Federated Phenotyping and Patient Representation Learning</i><br>
 
Dianbo Liu, Dmitriy Dligach, Timothy Miller</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Transfer Learning for Causal Sentence Detection</i><br>
 
Manolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev, Joan Ginés i Ametllé</td></tr>
 
<tr><td valign=top style="padding-top: 14px;"><b>12:00&#8211;12:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 2: Ontology and Typology</b></td></tr>
 
<tr><td valign=top width=100>12:00&#8211;12:15</td><td valign=top align=left><i>Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors</i><br>
 
Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan McDonald, Marianna Apidianaki</td></tr>
 
<tr><td valign=top width=100>12:15&#8211;12:30</td><td valign=top align=left><i>Simplification-induced transformations: typology and some characteristics</i><br>
 
Anaïs Koptient, Rémi Cardon, Natalia Grabar</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: Literature mining approaches and models</b></td></tr>
 
<tr><td valign=top width=100>14:00&#8211;14:15</td><td valign=top align=left><i>ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing </i><br>
 
Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar</td></tr>
 
<tr><td valign=top width=100>14:15&#8211;14:30</td><td valign=top align=left><i>Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings</i><br>
 
Zenan Zhai, Dat Quoc Nguyen, Saber Akhondi, Camilo Thorne, Christian Druckenbrodt, Trevor Cohn, Michelle Gregory, Karin Verspoor</td></tr>
 
<tr><td valign=top width=100>14:30&#8211;14:45</td><td valign=top align=left><i>Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning</i><br>
 
Hassan Alhuzali and Sophia Ananiadou</td></tr>
 
<tr><td valign=top width=100>14:45&#8211;15:00</td><td valign=top align=left><i>Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations</i><br>
 
Gaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov, Roland Roller</td></tr>
 
<tr><td valign=top width=100>15:00&#8211;15:15</td><td valign=top align=left>Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications<i></i><br>
 
Anna Koroleva and Patrick Paroubek</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;17:00</b></td><td valign=top style="padding-top: 14px;">Session 4: Shared Task</td></tr>
 
<tr><td valign=top width=100>16:00&#8211;16:15</td><td valign=top align=left><i>Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering</i><br>
 
Asma Ben Abacha, Chaitanya Shivade and Dina Demner-Fushman</td></tr>
 
<tr><td valign=top width=100>16:15&#8211;16:30</td><td valign=top align=left><i>PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation</i><br>
 
Wei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni and Guotong Xie</td></tr>
 
<tr><td valign=top width=100>16:30&#8211;16:45</td><td valign=top align=left><i>Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment</i><br>
 
Hemant Pugaliya, Karan Saxena, Shefali Garg, Sheetal Shalini, Prashant Gupta, Eric Nyberg and Teruko Mitamura</td></tr>
 
<tr><td valign=top width=100>16:45&#8211;17:00</td><td valign=top align=left><i>DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain</i><br>
 
Yichong Xu, Xiaodong Liu, Chunyuan Li, Hoifung Poon and Jianfeng Gao</td></tr>
 
<tr><td valign=top style="padding-top: 14px;"><b>17:00&#8211;18:00</b></td><td valign=top style="padding-top: 14px;"><b>Shared Task Poster Session</b></td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model</i><br>
 
Jiin Nam, Seunghyun Yoon and Kyomin Jung</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference</i><br>
 
Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian and Fei Xia</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI</i><br>
 
Cemil Cengiz, Ulaş Sert and Deniz Yuret</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering</i><br>
 
Huiwei Zhou, Xuefei Li, Weihong Yao, Chengkun Lang and Shixian Ning</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering</i><br>
 
Huiwei Zhou, Bizun Lei, Zhe Liu and Zhuang Liu</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations</i><br>
 
Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura and Eric Nyberg</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment</i><br>
 
Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg and Teruko Mitamura</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier</i><br>
 
Prakhar Sharma and Sumegh Roychowdhury</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge</i><br>
 
Vincent Nguyen, Sarvnaz Karimi and Zhenchang Xing</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.</i><br>
 
Sahil Chopra, Ankita Gupta and Anupama Kaushik</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain</i><br>
 
Noha Tawfik and Marco Spruit</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference</i><br>
 
William Kearns, Wilson Lau and Jason Thomas</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference</i><br>
 
Kamal raj Kanakarajan, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana, Soham Chatterjee and Malaikannan Sankarasubbu</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering</i><br>
 
Dibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh and Asif Ekbal</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition</i><br>
 
Andre Lamurias and Francisco M Couto</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model</i><br>
 
Lung-Hao Lee, Yi Lu, Po-Han Chen, Po-Lei Lee and Kuo-Kai Shyu</td></tr>
 
<tr><td valign=top width=100>&nbsp;</td><td valign=top align=left><i>ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System</i><br>
 
Anumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi, Sowmya Kamath and Anand Kumar</td></tr>
 
</table>
 
  
 
===Program Committee===
 
===Program Committee===
  
  * Hadi Amiri, Harvard Medical School, USA
 
 
   * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK  
 
   * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK  
   * Emilia Apostolova, Language.ai, USA
+
   * Emilia Apostolova, Anthem, Inc., USA
 
   * Eiji Aramaki, University of Tokyo, Japan  
 
   * Eiji Aramaki, University of Tokyo, Japan  
   * Asma Ben Abacha, US National Library of Medicine
+
   * Saadullah Amin, Saarland University, Germany
  * Cosmin (Adi) Bejan, Vanderbilt University, Nashville, TN
+
   * Steven Bethard, University of Arizona, USA
   * Siamak Barzegar, Barcelona Supercomputing Center, Spain
 
 
   * Olivier Bodenreider, US National Library of Medicine  
 
   * Olivier Bodenreider, US National Library of Medicine  
   * Leonardo Campillos Llanos, Universidad Autónoma de Madrid, Spain
+
   * Robert Bossy, Inrae, Université Paris Saclay, France
  * Qingyu Chen, US National Library of Medicine 
+
   * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain
   * Fenia Christopoulou, National Centre for Text Mining and University of Manchester, UK
 
  * Aaron Cohen, Oregon Health & Science University, USA
 
 
   * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA  
 
   * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA  
   * Brian Connolly, Kroger Digital, USA  
+
   * Brian Connolly, Ohio, USA
   * Viviana Cotik, University of Buenos Aires, Argentina
+
   * 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  
 
   * Dina Demner-Fushman, US National Library of Medicine  
   * Travis Goodwin, The University of Texas at Dallas, USA
+
  * Bart Desmet, National Institutes of Health, USA
   * Natalia Grabar, CNRS, France  
+
  * Dmitriy Dligach, Loyola University Chicago, USA
   * Cyril Grouin, LIMSI - CNRS, France
+
  * Kathleen C. Fraser, National Research Council Canada
   * Tudor Groza, The Garvan Institute of Medical Research, Australia
+
   * Travis Goodwin, Amazon Web Services (AWS), Seattle, Washington, USA
   * Sadid Hasan, Philips Research, Cambridge, MA
+
   * 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
 
   * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
   * Meizhi Ju, National Centre for Text Mining and University of Manchester, UK
+
   * Sarvnaz Karimi, CSIRO, Australia
   * Will Kearns, University of Washington, USA
+
   * Nazmul Kazi, University of North Florida, USA
   * Halil Kilicoglu, US National Library of Medicine
+
   * Roman Klinger, University of Stuttgart, Germany
  * Ari Klein, University of Pennsylvania, USA
+
   * Anna Koroleva, Omdena
   * Zfania Tom Korach, Harvard Medical School, USA
+
   * Majid Latifi, Department of Computer Science, University of York, York, UK
   * André Lamúrias, University of Lisbon, Portugal
+
   * Andre Lamurias, Aalborg University, Denmark
   * Majid Latifi, Trinity College Dublin, Ireland
 
 
   * Alberto Lavelli, FBK-ICT, Italy
 
   * Alberto Lavelli, FBK-ICT, Italy
 
   * Robert Leaman, US National Library of Medicine  
 
   * Robert Leaman, US National Library of Medicine  
 +
  * Lung-Hao Lee, National Central University, Taiwan
 
   * Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany  
 
   * Ulf Leser, Humboldt-Universit&auml;t zu Berlin, Germany  
  * Gal Levy-Fix, Columbia University, NY
+
   * Timothy Miller, Boston Childrens Hospital and Harvard Medical School, USA
  * Maolin Li, National Centre for Text Mining and University of Manchester, UK
+
   * Claire Nedellec, French national institute of agronomy (INRA)
  * Ramon Maldonado, The University of Texas at Dallas, USA
+
   * Guenter Neumann, German Research Center for Artificial Intelligence (DFKI)
   * Timothy Miller, Children’s Hospital Boston, USA  
+
   * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany
   * Danielle L Mowery, VA Salt Lake City Health Care System, USA
+
   * Nhung Nguyen, National Centre for Text Mining, University of Manchester, UK
  * Yassine M'Rabet, US National Library of Medicine
+
   * Aurélie Névéol, CNRS, France
   * Aurelie Neveol, LIMSI - CNRS, France
+
   * Amandalynne Paullada, University of Washington School of Medicine
  * Claire Nédellec, INRA, France
+
   * Yifan Peng, Weill Cornell Medical College, USA
   * Mariana Neves, German Federal Institute for Risk Assessment, Germany  
+
   * Laura Plaza, Universidad Nacional de Educación a Distancia
   * Denis Newman-Griffis, Clinical Center, National Institutes of Health, USA
 
  * Nhung Nguyen, The University of Manchester, UK
 
   * Karen O'Connor, University of Pennsylvania, USA
 
   * Yifan Peng, US National Library of Medicine  
 
   * Laura Plaza, UNED, Madrid, Spain
 
   * Sampo Pyysalo, University of Cambridge, UK
 
  * Alastair Rae, US National Library of Medicine
 
 
   * Francisco J. Ribadas-Pena, University of Vigo, Spain
 
   * 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  
 
   * Kirk Roberts, The University of Texas Health Science Center at Houston, USA  
   * Roland Roller, DFKI GmbH, Berlin, Germany
+
   * Roland Roller, DFKI, Germany
   * Sumegh Roychowdhury, Indian Institute of Technology Kharagpur
+
   * Mourad Sarrouti, Sumitovant Biopharma, Inc., USA
  * Max Savery, US National Library of Medicine
 
  * Chaitanya Shivade, IBM Research, Almaden, USA
 
 
   * Diana Sousa, University of Lisbon, Portugal
 
   * Diana Sousa, University of Lisbon, Portugal
   * Noha Seddik Tawfik, Arab Academy for Science and Technology, Egypt
+
   * Peng Su, University of Delaware, USA
   * Thy Thy Tran, National Centre for Text Mining and University of Manchester, UK
+
   * Madhumita Sushil, University of California, San Francisco, USA
   * Sumithra Velupillai, King’s College London, UK
+
   * Mario Sänger, Humboldt Universität zu Berlin, Germany
   * Davy Weissenbacher, University of Pennsylvania, USA
+
   * Andrew Taylor, Yale University School of Medicine, USA
   * W John Wilbur, US National Library of Medicine
+
   * Karin Verspoor, RMIT University, Australia
   * Shankai Yan, US National Library of Medicine
+
   * Leon Weber, Humboldt Universität Berlin, Germany
   * Amir Yazdavar, Wright State University, USA
+
   * Nathan M. White, James Cook University, Australia
   * Chrysoula Zerva, National Centre for Text Mining and University of Manchester, UK
+
  * Dustin Wright, University of Copenhagen,Denmark
   * Ayah Zirikly, Clinical Center, National Institutes of Health, USA
+
   * Amelie Wührl, University of Stuttgart, Germany
   * Seyedjamal Zolhavarieh, The University of Auckland, NZ
+
   * 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
 
   * Pierre Zweigenbaum, LIMSI - CNRS, France
  
===Organizers===
 
  Kevin Bretonnel Cohen, University of Colorado School of Medicine
 
  Dina Demner-Fushman, US National Library 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 and University of Manchester, UK
 
  
===WORKSHOP OVERVIEW AND SCOPE===
+
====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.
  
The ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in  
+
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”).
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:
+
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.
* 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
 
* Research Reproducibility
 
  
===SUBMISSION INSTRUCTIONS===
+
<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
  
Three types of submissions are invited: full papers, short papers and MEDIQA shared task participants' reports.
+
Detailed descriptions of the motivation, the tasks, and the data are also published in:
  
Full papers should not exceed eight (8) pages of text, plus unlimited references.  
+
* Goldsack, T., Zhang, Z., Lin, C., Scarton, C.. Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature. EMNLP 2022.  
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.  
+
* Luo, Z., Xie, Q., Ananiadou, S.. Readability Controllable Biomedical Document Summarization. EMNLP 2022 Findings.
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.
 
  
MEDIQA shared task participants reports should conform to the long paper submission guidelines.  
+
<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.
  
====Electronic Submission====
 
Submissions must be electronic and in PDF format, using the Softconf START conference management system at    https://www.softconf.com/acl2019/bionlp/
 
We strongly recommend consulting the ACL 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. Please see information about paper formatting requirements and style  at http://www.acl2019.org/EN/call-for-papers.xhtml. Scroll down to “Paper Submission and Templates.”
 
  
<b>Submissions need to be anonymous.</b>
 
  
====Dual submission policy====
+
<!--
Papers may NOT be submitted to the BioNLP 2019 workshop if they are or will be concurrently submitted to another meeting or publication.
 
  
 
=== MEDIQA 2019 ===   
 
=== MEDIQA 2019 ===   
Line 1,089: Line 1,142:
  
 
<b>Submissions should be anonymous. </b>
 
<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. -->
+
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.  
 
   
 
   
 
<!--
 
<!--
Line 1,128: Line 1,181:
 
   * Pierre Zweigenbaum, LIMSI - CNRS, France
 
   * Pierre Zweigenbaum, LIMSI - CNRS, France
  
 
<b>Organizers</b>
 
  * Kevin Bretonnel Cohen, University of Colorado School of Medicine
 
  * Dina Demner-Fushman, US National Library 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
 
  
  

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.