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| * June 12, 2023: Pre-recorded video due | | * June 12, 2023: Pre-recorded video due |
| * BioNLP 2023 Workshop at ACL, July 13 OR 14, 2023, Toronto, Canada | | * BioNLP 2023 Workshop at ACL, July 13 OR 14, 2023, Toronto, Canada |
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− | <!-- OLD PROGRAM
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− | <h2>BioNLP 2022 Program</h2>
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− |
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− | <h3>All times are Ireland timezone (GMT+1)</h3>
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− | <table cellspacing="0" cellpadding="5" border="0" valuing="top" width="95%">
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− | <tr>
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− | <td>09:00–09:10</td><td><b>Opening remarks</b></td>
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− | </tr>
| |
− | <tr>
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− | <td nowrap valign=top bgcolor=#ededed><b>09:10–10:30</b></td>
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− | <td valign=top bgcolor=#ededed>
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− | <b>Session 1: Question Answering, Discourse Structure and Clinical Applications (Onsite oral presentations) </b>
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− | </td>
| |
− | </tr>
| |
− | <tr>
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− | <td nowrap valign=top>09:10–9:30 </td>
| |
− | <td valign=top><b>Explainable Assessment of Healthcare Articles with QA</b>
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− | <br> <i>Alodie Boissonnet<sup>1</sup>, Marzieh Saeidi<sup>2</sup>, Vassilis Plachouras<sup>2</sup>, Andreas Vlachos<sup>1</sup></i><br>
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− | <sup>1</sup>University of Cambridge, <sup>2</sup>Facebook
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top>09:30–9:50</td>
| |
− | <td valign=top><b>A sequence-to-sequence approach for document-level relation extraction</b>
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− | <br>
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− | <i>John Giorgi, Gary Bader, Bo Wang</i><br>
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− | University of Toronto
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> 09:50–10:10 </td>
| |
− | <td valign=top> <b>Position-based Prompting for Health Outcome Generation</b>
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− | <br>
| |
− | <i>Micheal Abaho<sup>1</sup>, Danushka Bollegala<sup>2</sup>, Paula Williamson<sup>1</sup>, Susanna Dodd<sup>1</sup></i><br>
| |
− | <sup>1</sup>University of Liverpool, <sup>2</sup>University of Liverpool/Amazon
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> 10:10-10:30</td>
| |
− | <td valign=top>
| |
− | <b>How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection</b>
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− | <br>
| |
− | <i>Shahla Farzana, Ashwin Deshpande, Natalie Parde</i><br>
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− | University of Illinois at Chicago
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top bgcolor=#ededed><b>10:30–11:00</b></td>
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− | <td valign=top bgcolor=#ededed><b><em>Coffee Break</em></b></td>
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− | </tr>
| |
− | <tr>
| |
− | <td valign=top bgcolor=#ededed><b>11:00–12:30</b></td>
| |
− | <td valign=top bgcolor=#ededed><b>Hybrid Poster Session 1</b></td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Data Augmentation for Biomedical Factoid Question Answering</b>
| |
− | <br>
| |
− | <em>Dimitris Pappas, Prodromos Malakasiotis, Ion Androutsopoulos</em><br>
| |
− | Athens University of Economics and Business
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Slot Filling for Biomedical Information Extraction</b>
| |
− | <br>
| |
− | <em>Yannis Papanikolaou, Marlene Staib, Justin Grace, Francine Bennett</em><br>
| |
− | Healx Ltd
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations</b>
| |
− | <br>
| |
− | <em>Sihang Zeng, Zheng Yuan, Sheng Yu</em><br>
| |
− | Tsinghua University
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model</b>
| |
− | <br>
| |
− | <em>Hongyi Yuan<sup>1</sup>, Zheng Yuan<sup>1</sup>, Ruyi Gan<sup>2</sup>, Jiaxing Zhang<sup>2</sup>, Yutao Xie<sup>2</sup>, Sheng Yu<sup>1</sup></em><br>
| |
− | <sup>1</sup>Tsinghua University, <sup>2</sup>International Digital Economy Academy
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation</b>
| |
− | <br>
| |
− | <em>Usman Naseem<sup>1</sup>, Ajay Bandi<sup>2</sup>, Shaina Raza<sup>3</sup>, Junaid Rashid<sup>4</sup>, Bharathi Raja Chakravarthi<sup>5</sup></em><br>
| |
− | <sup>1</sup>University of Sydney, <sup>2</sup>Northwest Missouri State University, USA, <sup>3</sup>University of Toronto, Canada, <sup>4</sup>Kongju National University, South Korea, <sup>5</sup>National University of Ireland Galway
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation</b>
| |
− | <br>
| |
− | <em>Sixing Yan</em><br>
| |
− | Hong Kong Baptist University
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts</b>
| |
− | <br>
| |
− | <em>Uyen Phan<sup>1</sup> and Nhung Nguyen<sup>2</sup></em><br>
| |
− | <sup>1</sup>VNUHCM-University of Science, <sup>2</sup>The University of Manchester
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data</b>
| |
− | <br>
| |
− | <em>Taiki Watanabe<sup>1</sup>, Tomoya Ichikawa<sup>2</sup>, Akihiro Tamura<sup>2</sup>, Tomoya Iwakura<sup>3</sup>, Chunpeng Ma<sup>1</sup>, Tsuneo Kato<sup>2</sup></em><br>
| |
− | <sup>1</sup>Fujitsu Ltd., <sup>2</sup>Doshisha University, <sup>3</sup>Fujitsu
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study</b>
| |
− | <br>
| |
− | <em>Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu, Xiaopu Zhou, Tze Wing Mak, Yuk Yu Ip, Pascale Fung</em><br>
| |
− | The Hong Kong University of Science and Technology, Hong Kong, China
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Biomedical NER using Novel Schema and Distant Supervision</b>
| |
− | <br>
| |
− | <em>Anshita Khandelwal, Alok Kar, Veera Chikka, Kamalakar Karlapalem</em><br>
| |
− | International Institute of Information Technology
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models</b>
| |
− | <br>
| |
− | <em>Naoki Iinuma, Makoto Miwa, Yutaka Sasaki</em><br>
| |
− | Toyota Technological Institute
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Named Entity Recognition for Cancer Immunology Research Using Distant Supervision</b>
| |
− | <br>
| |
− | <em>Hai-Long Trieu<sup>1</sup>, Makoto Miwa<sup>2</sup>, Sophia Ananiadou<sup>3</sup></em><br>
| |
− | <sup>1</sup>National Institute of Advanced Industrial Science and Technology, <sup>2</sup>Toyota Technological Institute, <sup>3</sup>University of Manchester
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction</b>
| |
− | <br>
| |
− | <em>Christian Witte and Philipp Cimiano</em><br>
| |
− | Bielefeld University
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Pretrained Biomedical Language Models for Clinical NLP in Spanish</b>
| |
− | <br>
| |
− | <em>Casimiro Pio Carrino, Joan Llop, Marc Pàmies, Asier Gutiérrez-Fandiño, Jordi Armengol-Estapé, Joaquín Silveira-Ocampo, Alfonso Valencia, Aitor Gonzalez-Agirre, Marta Villegas</em><br>
| |
− | Barcelona Supercomputing Center
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training</b>
| |
− | <br>
| |
− | <em>Amir Soleimani<sup>1</sup>, Vassilina Nikoulina<sup>2</sup>, Benoit Favre<sup>3</sup>, Salah Ait Mokhtar<sup>2</sup></em><br>
| |
− | <sup>1</sup>University of Amsterdam, <sup>2</sup>Naver Labs Europe, <sup>3</sup>Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts</b>
| |
− | <br>
| |
− | <em>Saadullah Amin<sup>1</sup>, Noon Pokaratsiri Goldstein<sup>2</sup>, Morgan Wixted<sup>3</sup>, Alejandro Garcia-Rudolph<sup>4</sup>, Catalina Martínez-Costa<sup>5</sup>, Guenter Neumann<sup>1</sup></em><br>
| |
− | <sup>1</sup>DFKI ;amp; Saarland University, <sup>2</sup>DFKI, <sup>3</sup>Saarland University, <sup>4</sup>Institut Guttmann, <sup>5</sup>University of Murcia
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video Classification</b>
| |
− | <br>
| |
− | <em>Bin Li<sup>1</sup>, Yixuan Weng<sup>2</sup>, Fei Xia<sup>3</sup>, Bin Sun<sup>1</sup>, Shutao Li<sup>1</sup></em><br>
| |
− | <sup>1</sup>Hunan University, <sup>2</sup>Institute of Automation, Chinese Academy of Sciences, <sup>3</sup>1National Laboratory of Pattern Recognition,Institute of Automation 2University of Chinese Academy of Sciences, Beijing, China
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
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− | <td valign=top bgcolor=#ededed>
| |
− | <b>12:30–14:00</b>
| |
− | </td>
| |
− | <td valign=top bgcolor=#ededed>
| |
− | <b><em>Lunch Break</em></b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top bgcolor=#ededed>14:00–15:00</td>
| |
− | <td valign=top bgcolor=#ededed>
| |
− | <b> Summarization and text mining (Onsite oral presentations) </b>
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top> 14:00-14:20</td>
| |
− | <td>
| |
− | <b>GenCompareSum: a hybrid unsupervised summarization method using salience</b>
| |
− | <br>
| |
− | <em>Jennifer Bishop, Qianqian Xie, Sophia Ananiadou</em><br>
| |
− | University of Manchester
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top> 14:20-14:40</td>
| |
− | <td>
| |
− | <b>Low Resource Causal Event Detection from Biomedical Literature</b>
| |
− | <br>
| |
− | <em>Zhengzhong Liang, Enrique Noriega-Atala, Clayton Morrison, Mihai Surdeanu</em><br>
| |
− | The University of Arizona
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td valign=top bgcolor=#ededed><b>15:00–15:30</b></td>
| |
− | <td valign=top bgcolor=#ededed>
| |
− | <b><em>Coffee Break</em></b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top bgcolor=#ededed>15:30–17:00</td>
| |
− | <td valign=top bgcolor=#ededed>
| |
− | <b> Hybrid Poster Session 2 </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering</b>
| |
− | <br>
| |
− | <em>Deepak Gupta and Dina Demner-Fushman</em><br>
| |
− | National Library of Medicine, NIH
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <td nowrap valign=top> </td>
| |
− | <td>
| |
− | <b>BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles</b><br>
| |
− | <em>Sudipta Singha Roy and Robert E. Mercer </em><br>
| |
− | The University of Western Ontario
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations</b>
| |
− | <br>
| |
− | <em>Russell Richie<sup>1</sup>, Sachin Grover<sup>1</sup>, Fuchiang Tsui<sup>2</sup></em><br>
| |
− | <sup>1</sup>Children's Hospital of Philadelphia, <sup>2</sup>Children's Hospital of Philadelphia; University of Pennsylvania
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues</b>
| |
− | <br>
| |
− | <em>Avisha Das<sup>1</sup>, Salih Selek<sup>2</sup>, Alia Warner<sup>2</sup>, Xu Zuo<sup>1</sup>, Yan Hu<sup>1</sup>, Vipina Kuttichi Keloth<sup>1</sup>, Jianfu Li<sup>1</sup>, W. Zheng<sup>1</sup>, Hua Xu<sup>1</sup></em><br>
| |
− | <sup>1</sup>School of Biomedical Informatics, UTHealth, <sup>2</sup>McGovern Medical School, UTHealth
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>BanglaBioMed: A Biomedical Named-Entity Annotated Corpus for Bangla (Bengali)</b>
| |
− | <br>
| |
− | <em>Salim Sazzed</em><br>
| |
− | Old Dominion University
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering</b>
| |
− | <br>
| |
− | <em>Xing David Wang, Ulf Leser, Leon Weber</em><br>
| |
− | Humboldt-Universität zu Berlin
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection</b>
| |
− | <br>
| |
− | <em>Bosung Kim and Ndapa Nakashole</em><br>
| |
− | University of California, San Diego
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>ICDBigBird: A Contextual Embedding Model for ICD Code Classification</b>
| |
− | <br>
| |
− | <em>George Michalopoulos<sup>1</sup>, Michal Malyska<sup>2</sup>, Nicola Sahar<sup>3</sup>, Alexander Wong<sup>1</sup>, Helen Chen<sup>1</sup></em><br>
| |
− | <sup>1</sup>University of Waterloo, <sup>2</sup>University of Toronto, <sup>3</sup>Semantic Health
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation</b>
| |
− | <br>
| |
− | <em>Hillary Ngai<sup>1</sup> and Frank Rudzicz<sup>2</sup></em><br>
| |
− | <sup>1</sup>Vector Institute for Artificial Intelligence, <sup>2</sup>Vector Institute for Artificial Intelligence, University of Toronto
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature</b>
| |
− | <br>
| |
− | <em>Anjani Dhrangadhariya<sup>1</sup> and Henning Müller<sup>2</sup></em><br>
| |
− | <sup>1</sup>HES-SO Valais-Wallis, <sup>2</sup>HES-SO
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Improving Romanian BioNER Using a Biologically Inspired System</b>
| |
− | <br>
| |
− | <em>Maria Mitrofan<sup>1</sup> and Vasile Pais<sup>2</sup></em><br>
| |
− | <sup>1</sup>RACAI, <sup>2</sup>Research Institute for Artificial Intelligence, Romanian Academy
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>EchoGen: Generating Conclusions from Echocardiogram Notes</b>
| |
− | <br>
| |
− | <em>Liyan Tang<sup>1</sup>, Shravan Kooragayalu<sup>2</sup>, Yanshan Wang<sup>2</sup>, Ying Ding<sup>1</sup>, Greg Durrett<sup>3</sup>, Justin Rousseau<sup>1</sup>, Yifan Peng<sup>4</sup></em><br>
| |
− | <sup>1</sup>University of Texas at Austin, <sup>2</sup>University of Pittsburgh, <sup>3</sup>UT Austin, <sup>4</sup>Cornell Medicine
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record</b>
| |
− | <br>
| |
− | <em>Kevin Xie<sup>1</sup>, Brian Litt<sup>2</sup>, Dan Roth<sup>1</sup>, Colin Ellis<sup>2</sup></em><br>
| |
− | <sup>1</sup>University of Pennsylvania, <sup>2</sup>Perelman School of Medicine, University of Pennsylvania
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets</b>
| |
− | <br>
| |
− | <em>Mourad Sarrouti, Carson Tao, Yoann Mamy Randriamihaja</em><br>
| |
− | Sumitovant Biopharma
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Utility Preservation of Clinical Text After De-Identification</b>
| |
− | <br>
| |
− | <em>Thomas Vakili<sup>1</sup> and Hercules Dalianis<sup>2</sup></em><br>
| |
− | <sup>1</sup>Department of Computer and Systems Sciences, Stockholm University, <sup>2</sup>DSV/Stockholm University
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding</b>
| |
− | <br>
| |
− | <em>Matúš Falis<sup>1</sup>, Hang Dong<sup>2</sup>, Alexandra Birch<sup>1</sup>, Beatrice Alex<sup>1</sup></em><br>
| |
− | <sup>1</sup>The University of Edinburgh, <sup>2</sup>Oxford University
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Towards Automatic Curation of Antibiotic Resistance Genes via Statement Extraction from Scientific Papers: A Benchmark Dataset and Models</b>
| |
− | <br>
| |
− | <em>Sidhant Chandak<sup>1</sup>, Liqing Zhang<sup>2</sup>, Connor Brown<sup>2</sup>, Lifu Huang<sup>2</sup></em><br>
| |
− | <sup>1</sup>Indian institute of Technology Kanpur, <sup>2</sup>Virginia Tech
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Model Distillation for Faithful Explanations of Medical Code Predictions</b>
| |
− | <br>
| |
− | <em>Zach Wood-Doughty, Isabel Cachola, Mark Dredze</em><br>
| |
− | Johns Hopkins University
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>Towards Generalizable Methods for Automating Risk Score Calculation</b>
| |
− | <br>
| |
− | <em>Jennifer J Liang<sup>1</sup>, Eric Lehman<sup>2</sup>, Ananya Iyengar<sup>3</sup>, Diwakar Mahajan<sup>1</sup>, Preethi Raghavan<sup>1</sup>, Cindy Y. Chang<sup>4</sup>, Peter Szolovits<sup>2</sup></em><br>
| |
− | <sup>1</sup>IBM Research, <sup>2</sup>MIT, <sup>3</sup>Northeastern University, <sup>4</sup>Brigham and Women's Hospital
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | <tr>
| |
− | <td nowrap valign=top>
| |
− |
| |
− | </td>
| |
− | <td>
| |
− | <b>DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem</b>
| |
− | <br>
| |
− | <em>Wojciech Kusa<sup>1</sup>, Georgios Peikos<sup>2</sup>, Óscar Espitia<sup>3</sup>, Allan Hanbury<sup>1</sup>, Gabriella Pasi<sup>4</sup></em><br>
| |
− | <sup>1</sup>TU Wien, <sup>2</sup>University of Milano-Bicocca, <sup>3</sup>University of Milano Bicocca, <sup>4</sup>Università degli Studi di Milano Bicocca
| |
− | </td>
| |
− | </tr>
| |
− |
| |
− | </table>
| |
− |
| |
− | ===Submission Types & Requirements ===
| |
− |
| |
− | Following the previous conferences, BioNLP 2022 will be open for two types of submissions: long and short papers. Please follow ACL guidelines https://acl-org.github.io/ACLPUB/formatting.html
| |
− | and templates: https://github.com/acl-org/acl-style-files
| |
− |
| |
− | Overleaf templates: https://www.overleaf.com/project/5f64f1fb97c4c50001b60549
| |
− | -->
| |
| | | |
| ===WORKSHOP OVERVIEW AND SCOPE=== | | ===WORKSHOP OVERVIEW AND SCOPE=== |
Line 550: |
Line 40: |
| * Translating NLP research into practice; | | * Translating NLP research into practice; |
| * Getting reproducible results. | | * Getting reproducible results. |
| + | |
| + | |
| | | |
| ===Program Committee=== | | ===Program Committee=== |
Line 703: |
Line 195: |
| | | |
| | | |
− | <!-- The first challenge on Medical Video Question Answering is collocated with the BioNLP 2022 Workshop. MedVidQA focuses on providing relevant segments of videos as answers to health-related questions. Medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions.
| |
− | Please check the challenge website for details on the tasks, datasets, and submission guidelines: https://medvidqa.github.io
| |
| | | |
− | <!--
| |
− | ===Program ===
| |
| | | |
− | <h3>All times are in Pacific Time (Seattle, San Francisco, Los Angeles)</h3>
| |
| | | |
− | Friday June 11, 2021
| + | ====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. |
− | <table cellspacing="0" cellpadding="5" border="0">
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">08:00–08:15</td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Opening remarks</b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">08:15–09:15 </td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 1: Information Extraction </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>08:15–08:30</td>
| |
− | <td valign=top align=left>
| |
− | <i>Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction</i> <br>Peng Su, Yifan Peng and K. Vijay-Shanker
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>08:30–08:45</td>
| |
− | <td valign=top align=left>
| |
− | <i>Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization</i>
| |
− | <br>Dongfang Xu and Steven Bethard
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>08:45–09:00</td>
| |
− | <td valign=top align=left>
| |
− | <i>Scalable Few-Shot Learning of Robust Biomedical Name Representations</i>
| |
− | <br>Pieter Fivez, Simon Suster and Walter Daelemans
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>09:00–09:15</td>
| |
− | <td valign=top align=left>
| |
− | <i>SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN</i>
| |
− | <br>Gjorgjina Cenikj, Tome Eftimov and Barbara Koroušić Seljak
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">09:15–10:00 </td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 2: Clinical NLP </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>09:15–09:30</td>
| |
− | <td valign=top align=left>
| |
− | <i>Are we there yet? Exploring clinical domain knowledge of BERT models</i>
| |
− | <br>Madhumita Sushil, Simon Suster and Walter Daelemans
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>09:30–09:45</td>
| |
− | <td valign=top align=left>
| |
− | <i>Towards BERT-based Automatic ICD Coding: Limitations and Opportunities</i>
| |
− | <br>Damian Pascual, Sandro Luck and Roger Wattenhofer
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>09:45–10:00</td>
| |
− | <td valign=top align=left>
| |
− | <i>emrKBQA: A Clinical Knowledge-Base Question Answering Dataset</i>
| |
− | <br>Preethi Raghavan, Jennifer J Liang, Diwakar Mahajan, Rachita Chandra and Peter Szolovits
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>10:00–10:30</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;">10:30–11:00</td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 3: MEDIQA 2021 Overview: Asma Ben Abacha </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>10:30–11:00</td>
| |
− | <td valign=top align=left>
| |
− | <i>Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain</i>
| |
− | <br>Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz and Dina Demner-Fushman
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">11:00–12:00 </td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 4: MEDIQA 2021 Presentations </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>11:00–11:15</td>
| |
− | <td valign=top align=left>
| |
− | <i>WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers</i>
| |
− | <br>Mario Sänger, Leon Weber and Ulf Leser
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>11:15–11:30</td>
| |
− | <td valign=top align=left>
| |
− | <i>paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization</i>
| |
− | <br>Wei Zhu, Yilong He, Ling Chai, Yunxiao Fan, Yuan Ni, GUOTONG XIE and Xiaoling Wang
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>11:30–11:45</td>
| |
− | <td valign=top align=left>
| |
− | <i>BDKG at MEDIQA 2021: System Report for the Radiology Report Summarization Task</i>
| |
− | <br>Songtai Dai, Quan Wang, Yajuan Lyu and Yong Zhu
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top width=100>11:45–12:00</td>
| |
− | <td valign=top align=left>
| |
− | <i>damo_nlp at MEDIQA 2021: Knowledge-based Preprocessing and Coverage-oriented Reranking for Medical Question Summarization</i>
| |
− | <br>Yifan He, Mosha Chen and Songfang Huang
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>12:00–12:30</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;">12:30–14:30</td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 5: Poster session 1 </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Stress Test Evaluation of Biomedical Word Embeddings</i>
| |
− | <br>Vladimir Araujo, Andrés Carvallo, Carlos Aspillaga, Camilo Thorne and Denis Parra
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>BLAR: Biomedical Local Acronym Resolver</i>
| |
− | <br>William Hogan, Yoshiki Vazquez Baeza, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim and Chun-Nan Hsu
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Claim Detection in Biomedical Twitter Posts</i>
| |
− | <br>Amelie Wührl and Roman Klinger
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>BioELECTRA:Pretrained Biomedical text Encoder using Discriminators</i>
| |
− | <br>Kamal raj Kanakarajan, Bhuvana Kundumani and Malaikannan Sankarasubbu
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Word centrality constrained representation for keyphrase extraction</i>
| |
− | <br>
| |
− | Zelalem Gero and Joyce Ho
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>End-to-end Biomedical Entity Linking with Span-based Dictionary Matching</i>
| |
− | <br>Shogo Ujiie, Hayate Iso, Shuntaro Yada, Shoko Wakamiya and Eiji ARAMAKI
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Word-Level Alignment of Paper Documents with their Electronic Full-Text Counterparts</i>
| |
− | <br>Mark-Christoph Müller, Sucheta Ghosh, Ulrike Wittig and Maja Rey
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Improving Biomedical Pretrained Language Models with Knowledge</i>
| |
− | <br>Zheng Yuan, Yijia Liu, Chuanqi Tan, Songfang Huang and Fei Huang
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain</i>
| |
− | <br>Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard and Guergana Savova
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Contextual explanation rules for neural clinical classifiers</i>
| |
− | <br>Madhumita Sushil, Simon Suster and Walter Daelemans
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts</i>
| |
− | <br>Yang Liu, Yuanhe Tian, Tsung-Hui Chang, Song Wu, Xiang Wan and Yan Song
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA</i>
| |
− | <br>Sultan Alrowili and Vijay Shanker
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Semi-Supervised Language Models for Identification of Personal Health Experiential from Twitter Data: A Case for Medication Effects</i>
| |
− | <br>Minghao Zhu and Keyuan Jiang
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Context-aware query design combines knowledge and data for efficient reading and reasoning</i>
| |
− | <br>Emilee Holtzapple, Brent Cochran and Natasa Miskov-Zivanov
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Measuring the relative importance of full text sections for information retrieval from scientific literature.</i>
| |
− | <br>Lana Yeganova, Won Gyu KIM, Donald Comeau, W John Wilbur and Zhiyong Lu
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>14:30–15:00</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;">15:00–17:00 </td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 6: MEDIQA 2021 Poster Session </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization</i>
| |
− | <br>Khalil Mrini, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Emilia Farcas and Ndapa Nakashole
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>ChicHealth @ MEDIQA 2021: Exploring the limits of pre-trained seq2seq models for medical summarization</i>
| |
− | <br>Liwen Xu, Yan Zhang, Lei Hong, Yi Cai and Szui Sung
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>NCUEE-NLP at MEDIQA 2021: Health Question Summarization Using PEGASUS Transformers</i>
| |
− | <br>
| |
− | Lung-Hao Lee, Po-Han Chen, Yu-Xiang Zeng, Po-Lei Lee and Kuo-Kai Shyu
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain</i>
| |
− | <br>Spandana Balumuri, Sony Bachina and Sowmya Kamath S
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning</i>
| |
− | <br>Ravi Kondadadi, Sahil Manchanda, Jason Ngo and Ronan McCormack
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization</i>
| |
− | <br>Jean-Benoit Delbrouck, Cassie Zhang and Daniel Rubin
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization</i>
| |
− | <br>Shweta Yadav, Mourad Sarrouti and Deepak Gupta
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization</i>
| |
− | <br>Diwakar Mahajan, Ching-Huei Tsou and Jennifer J Liang
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model</i>
| |
− | <br>Duy-Cat Can, Quoc-An Nguyen, Quoc-Hung Duong, Minh-Quang Nguyen, Huy-Son Nguyen, Linh Nguyen Tran Ngoc, Quang-Thuy Ha and Mai-Vu Tran
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>MNLP at MEDIQA 2021: Fine-Tuning PEGASUS for Consumer Health Question Summarization</i>
| |
− | <br>Jooyeon Lee, Huong Dang, Ozlem Uzuner and Sam Henry
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top colspan=2 align=left>
| |
− | <i>UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization</i>
| |
− | <br>Hoang-Quynh Le, Quoc-An Nguyen, Quoc-Hung Duong, Minh-Quang Nguyen, Huy-Son Nguyen, Tam Doan Thanh, Hai-Yen Thi Vuong and Trang M. Nguyen
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">17:00–17:30</td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Session 7: Invited Talk by Makoto Miwa </b>
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td valign=top style="padding-top: 14px;">17:30–18:00 </td>
| |
− | <td valign=top style="padding-top: 14px;">
| |
− | <b>Closing remarks</b>
| |
− | </td>
| |
− | </tr>
| |
− | </table>
| |
| | | |
− | ===IMPORTANT DATES ===
| |
| | | |
− | *Submission deadline: March 20, 2021 11:59 PM Eastern US https://www.softconf.com/naacl2021/bionlp21/
| |
− | *Notification of acceptance: April 15, 2021
| |
− | *Camera-ready copy due from authors: April 26, 2021 ('''HARD DEADLINE''')
| |
− | *Workshop: June 11, 2021
| |
− |
| |
− |
| |
− |
| |
− | Final papers should match the NAACL 2021 style guide and instructions for formatting:
| |
− | https://2021.naacl.org/calls/style-and-formatting/
| |
− | General *ACL guidelines for formatting:
| |
− | https://acl-org.github.io/ACLPUB/formatting.html
| |
− |
| |
− | === Shared Task===
| |
− | <font size="4"><b>MEDIQA 2021</b></font>
| |
− | The second edition of the MEDIQA challenge collocated with the BioNLP 2021Workshop focuses on summarization in the medical domain with three tasks:
| |
− | * Consumer health question summarization
| |
− | * Multi-answer summarization
| |
− | * Radiology report summarization
| |
− | Please check the website for details on the tasks, datasets, and submission guidelines: https://sites.google.com/view/mediqa2021
| |
− |
| |
− | <!--
| |
− | ===Program Committee===
| |
− |
| |
− | * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
| |
− | * Emilia Apostolova, Language.ai, USA
| |
− | * Eiji Aramaki, University of Tokyo, Japan
| |
− | * Asma Ben Abacha, US National Library of Medicine
| |
− | * Steven Bethard, University of Arizona, USA
| |
− | * Olivier Bodenreider, US National Library of Medicine
| |
− | * Leonardo Campillos Llanos, Universidad Autónoma de Madrid, Spain
| |
− | * Qingyu Chen, US National Library of Medicine
| |
− | * Fenia Christopoulou, National Centre for Text Mining and University of Manchester, UK
| |
− |
| |
− | * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA
| |
− | * Brian Connolly, Kroger Digital, 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
| |
− | * William Kearns, UW Medicine, USA
| |
− | * Halil Kilicoglu, University of Illinois at Urbana-Champaign, USA
| |
− | * Ari Klein, University of Pennsylvania, USA
| |
− | * André Lamúrias, University of Lisbon, Portugal
| |
− | * Alberto Lavelli, FBK-ICT, Italy
| |
− | * Robert Leaman, US National Library of Medicine
| |
− | * Ulf Leser, Humboldt-Universität zu Berlin, Germany
| |
− | * Timothy Miller, Children’s Hospital Boston, USA
| |
− | * 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
| |
− | * Yifan Peng, Cornell Medical School, USA
| |
− | * Laura Plaza, UNED, Madrid, Spain
| |
− | * 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
| |
− | * Diana Sousa, University of Lisbon, Portugal
| |
− | * 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
| |
− | * Chrysoula Zerva, National Centre for Text Mining and University of Manchester, UK
| |
− | * Ayah Zirikly, Clinical Center, National Institutes of Health, USA
| |
− | * Pierre Zweigenbaum, LIMSI - CNRS, France
| |
| | | |
| <!-- | | <!-- |
− | ===Shared Task Program Committee===
| |
− | * Spandana Balumuri, National Institute of Technology Karnataka, Surathkal, India
| |
− | * Asma Ben Abacha, NLM/NIH
| |
− | * Yi Cai, Chic Health, Shanghai, China
| |
− | * Duy-Cat Can, University of Engineering and Technology, Vietnam
| |
− | * Songtai Dai, Baidu, Inc, Beijing, China
| |
− | * Jean-Benoit Delbrouck, Stanford University
| |
− | * Deepak Gupta, NLM/NIH
| |
− | * Yifan He, Alibaba Group, Sunnyvale, CA
| |
− | * Abdullah Faiz Ur Rahman Khilji, National Institute of Technology Silchar, Mumbai, India
| |
− | * Ravi Kondadadi, Optum
| |
− | * Jooyeon Lee, George Mason University, Fairfax, VA
| |
− | * Lung-Hao Lee, National Central University, Taiwan
| |
− | * Diwakar Mahajan, IBM Research, Yorktown Heights, NY
| |
− | * Yassine Mrabet, NLM/NIH
| |
− | * Khalil Mrini, University of California, San Diego
| |
− | * Mourad Sarrouti, NLM/NIH
| |
− | * Mario Sänger, Humboldt-Universität zu Berlin
| |
− | * Chaitanya Shivade, Amazon
| |
− | * Shweta Yadav, NLM/NIH
| |
− | * Yuhao Zhang, Stanford University
| |
− | * Wei Zhu, East China Normal University, Shanghai -->
| |
− |
| |
− | ===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
| |
− |
| |
− | <!-- END -->
| |
− |
| |
− | <!-- An ACL 2020 Workshop associated with the SIGBIOMED special interest group
| |
− |
| |
− | ===IMPORTANT DATES ===
| |
− |
| |
− | *Submission deadline: <del>Friday, March 20, 2020 </del> '''New: Friday, April, 3, 2020,''' 11:59 PM Eastern US
| |
− | 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'''
| |
− |
| |
− |
| |
− |
| |
− | <h2>The 19th Workshop on Biomedical Language Processing</h2>
| |
− | <table cellspacing="0" cellpadding="5" border="0" width="80%">
| |
− | <tr><td width=100> </td><td style="text-align:center"> ALL TIMES IN SEATTLE PT TIME ZONE</td></tr>
| |
− | <tr><td width=100>08:30–08:40</td><td style="text-align:center"> <b>Opening remarks</b></td></tr>
| |
− | <tr><td width=100>08:40–10:30</td><td style="text-align:center"> <b>Session 1: High accuracy information retrieval, spin and bias </b></td></tr>
| |
− | <tr><td width=100>08:40–09:10</td><td style="text-align:center"><b>Invited Talk</b><br><br> <em>Biomedical Retrieval: Users, Data, and Tasks</em><br><b><em>Kirk Roberts</em></b></td></tr>
| |
− | <tr><td width=100>09:10–09:20</td><td><i>Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings</i><br>Anthony Rios, Reenam Joshi and Hejin Shin</td></tr>
| |
− | <tr><td width=100>09:20–09:30</td><td><i>Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention</i><br>Manirupa Das, Juanxi Li, Eric Fosler-Lussier, Simon Lin, Steve Rust, Yungui Huang and Rajiv Ramnath</td></tr>
| |
− | <tr><td width=100>09:30–09:40</td><td><i>Interactive Extractive Search over Biomedical Corpora</i><br>Hillel Taub Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen and Yoav Goldberg</td></tr>
| |
− | <tr><td width=100>09:40–09:50</td><td><i>Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies</i><br>Amandalynne Paullada, Bethany Percha and Trevor Cohen</td></tr>
| |
− | <tr><td width=100>09:50–10:00</td><td><i>DeSpin: a prototype system for detecting spin in biomedical publications</i><br>Anna Koroleva, Sanjay Kamath, Patrick Bossuyt and Patrick Paroubek</td></tr>
| |
− | <tr><td width=100>10:00–10:30</td><td><b><em>Discussion</em></b></td></tr>
| |
− | <tr><td width=100>10:30–10:45</td><td style="text-align:center"><b><em>Coffee Break</em></b></td></tr>
| |
− | <tr><td width=100>10:45–13:00</td><td style="text-align:center"><b> Session 2: Clinical Language Processing </b></td></tr>
| |
− | <tr><td width=100>10:45–11:15</td><td style="text-align:center"><b>Invited Talk</b><br><em>Challenges in Domain Adaptation for Medical NLP</em><br><b><em>Tim Miller</em></b></td></tr>
| |
− | <tr><td width=100>11:15–11:25</td><td><i>Towards Visual Dialog for Radiology</i><br>Olga Kovaleva, Chaitanya Shivade, Satyananda Kashyap, Karina Kanjaria, Joy Wu, Deddeh Ballah, Adam Coy, Alexandros Karargyris, Yufan Guo, David Beymer, Anna Rumshisky and Vandana Mukherjee Mukherjee</td></tr>
| |
− | <tr><td width=100>11:25–11:35</td><td><i>A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction</i><br>Chen Lin, Timothy Miller, Dmitriy Dligach, Farig Sadeque, Steven Bethard and Guergana Savova</td></tr>
| |
− | <tr><td width=100>11:35–11:45</td><td><i>Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset</i><br>Thomas Searle, Zina Ibrahim and Richard Dobson</td></tr>
| |
− | <tr><td width=100>11:45–11:55</td><td><i>Comparative Analysis of Text Classification Approaches in Electronic Health Records</i><br>Aurelie Mascio, Zeljko Kraljevic, Daniel Bean, Richard Dobson, Robert Stewart, Rebecca Bendayan and Angus Roberts</td></tr>
| |
− | <tr><td width=100>11:55–12:05</td><td><i>Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning</i><br>Liyan Xu, Julien Hogan, Rachel E. Patzer and Jinho D. Choi</td></tr>
| |
− | <tr><td width=100>12:05–12:15</td><td><i>Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity</i><br>Yuxia Wang, Fei Liu, Karin Verspoor and Timothy Baldwin</td></tr>
| |
− | <tr><td width=100>12:15–12:45</td><td><b><em>Discussion</em></b></td></tr>
| |
− | <tr><td width=100>12:45–13:30</td><td style="text-align:center"><b><em>Lunch</em></b></td></tr>
| |
− | <tr><td width=100>13:30–15:30</td><td style="text-align:center"> <b> Session 3: Language Understanding </b></td></tr>
| |
− | <tr><td width=100>13:30–14:00</td><td style="text-align:center"><b>Invited Talk</b><br><em> </em><br><b><em> Anna Rumshisky</em></b></td></tr>
| |
− | <tr><td width=100>14:00–14:10</td><td><i>Entity-Enriched Neural Models for Clinical Question Answering</i><br>Bhanu Pratap Singh Rawat, Wei-Hung Weng, So Yeon Min, Preethi Raghavan and Peter Szolovits</td></tr>
| |
− | <tr><td width=100>14:10–14:20</td><td><i>Evidence Inference 2.0: More Data, Better Models</i><br>Jay DeYoung, Eric Lehman, Benjamin Nye, Iain Marshall and Byron C. Wallace</td></tr>
| |
− | <tr><td width=100>14:20–14:30</td><td><i>Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches</i><br>Ning Wang, Fan Luo, Vishal Peddagangireddy, Koduvayur Subbalakshmi and Rajarathnam Chandramouli</td></tr>
| |
− | <tr><td width=100>14:30–14:40</td><td><i>BioMRC: A Dataset for Biomedical Machine Reading Comprehension</i><br>Dimitris Pappas, Petros Stavropoulos, Ion Androutsopoulos and Ryan McDonald</td></tr>
| |
− | <tr><td width=100>14:40–14:50</td><td><i>Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Representation</i><br>Avi Bleiweiss</td></tr>
| |
− | <tr><td width=100>14:50–15:00</td><td><i>Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes</i><br>Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt and James Gilkerson</td></tr>
| |
− | <tr><td width=100>15:00–15:30</td><td><b><em>Discussion</em></b></td></tr>
| |
− | <tr><td width=100>15:30–15:45</td><td style="text-align:center"><b><em>Coffee Break</em></b></td></tr>
| |
− | <tr><td width=100>15:45–17:45</td><td style="text-align:center"><b> Session 4: Named Entity Recognition and Knowledge Representation </b></td>
| |
− | </tr>
| |
− | <tr><td width=100>15:45–16:25</td><td style="text-align:center"><b>Invited Talk</b><br> <em>Machine Reading for Precision Medicine</em><br><b><em>Hoifung Poon</em></b></td></tr>
| |
− | <tr><td width=100>16:25–16:35</td><td><i>Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings</i><br>David Chang, Ivana Balažević, Carl Allen, Daniel Chawla, Cynthia Brandt and Andrew Taylor</td></tr>
| |
− | <tr><td width=100>16:35–16:45</td><td><i>Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience</i><br>Isar Nejadgholi, Kathleen C. Fraser and Berry de Bruijn</td></tr>
| |
− | <tr><td width=100>16:45–16:55</td><td><i>A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction</i><br>Saadullah Amin, Katherine Ann Dunfield, Anna Vechkaeva and Guenter Neumann</td></tr>
| |
− | <tr><td width=100>16:55–17:05</td><td><i>Global Locality in Biomedical Relation and Event Extraction</i><br>Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong and David Martinez Iraola</td></tr>
| |
− | <tr><td width=100>17:05–17:15</td><td><i>An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining</i><br>Yifan Peng, Qingyu Chen and Zhiyong Lu</td></tr>
| |
− | <tr><td width=100>17:15–17:45</td><td><b><em>Discussion</em></b></td></tr>
| |
− | <tr><td width=100>17:45–18:00</td><td style="text-align:center"><b> Closing remarks</b></td></tr>
| |
− | </table>
| |
− |
| |
− |
| |
− |
| |
− | ===Program Committee===
| |
− |
| |
− | <!-- * Hadi Amiri, Harvard Medical School, USA
| |
− | * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
| |
− | * Emilia Apostolova, Language.ai, USA
| |
− | * Eiji Aramaki, University of Tokyo, Japan
| |
− | * Asma Ben Abacha, US National Library of Medicine
| |
− | <!-- * Cosmin (Adi) Bejan, Vanderbilt University, Nashville, TN
| |
− | <1-- * Siamak Barzegar, Barcelona Supercomputing Center, Spain
| |
− | <!-- * 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
| |
− | <1-- * Qingyu Chen, US National Library of Medicine
| |
− | * 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
| |
− | * 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-Universität 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
| |
− |
| |
− | -->
| |
− | <!--
| |
− |
| |
− | ===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
| |
− | language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP
| |
− | and exposing these researchers to the mainstream ACL research, and a venue for informing the mainstream ACL researchers about the fast growing and important domain.
| |
− | The workshop will continue presenting work on a broad and interesting range of topics in NLP.
| |
− |
| |
− | The active areas of research include, but are not limited to:
| |
− | * Entity identification and normalization (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===
| |
− |
| |
− | Two types of submissions are invited: full papers and short papers.
| |
− |
| |
− | Full 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.
| |
− |
| |
− | Please see https://acl2020.org/calls/papers/ for templates.
| |
− |
| |
− | -->
| |
− | <!-- to open later
| |
− | ====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 2023 workshop if they are or will be concurrently submitted to another meeting or publication.
| |
− |
| |
− | <!-- 2019
| |
− |
| |
− | <font size="4"><b>BIONLP 2019</b></font>
| |
− | <br/>
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− | <font size="3">Florence, Italy, Thursday, August 1, 2019</font>
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− |
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− | 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
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− | *Camera-ready copy due from authors: '''Friday, June 7''', 2019 -- '''Firm deadline due to ACL schedule'''.
| |
− | *'''Workshop: Thursday, August 1, 2019'''
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− |
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− |
| |
− | <h2>BioNLP 2019 WORKSHOP PROGRAM</h2>
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− |
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− | <table cellspacing="0" cellpadding="5" border="0"><tr><td colspan=2 style="padding-top: 14px;"><b>Thursday August 1, 2019</b></td></tr>
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− | <tr><td valign=top style="padding-top: 14px;"><b>8:30–8:45</b></td><td valign=top style="padding-top: 14px;"><b>Opening remarks</b></td></tr>
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− | <tr><td valign=top style="padding-top: 14px;"><b>8:45–10:30</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>8:45–9:00</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>
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− | <tr><td valign=top width=100>9:00–9:15</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>
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− | <tr><td valign=top width=100>9:15–9:30</td><td valign=top align=left><i>A Paraphrase Generation System for EHR Question Answering</i><br>
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− | Sarvesh Soni and Kirk Roberts</td></tr>
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− | <tr><td valign=top width=100>9:30–9:45</td><td valign=top align=left><i>REflex: Flexible Framework for Relation Extraction in Multiple Domains</i><br>
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− | Geeticka Chauhan, Matthew McDermott, Peter Szolovits</td></tr>
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− | <tr><td valign=top width=100>9:45–10:00</td><td valign=top align=left><i>Analysing Representations of Memory Impairment in a Clinical Notes Classification Model</i><br>
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− | Mark Ormerod, Jesús Martínez-del-Rincón, Neil Robertson, Bernadette McGuinness, Barry Devereux</td></tr>
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− | <tr><td valign=top width=100>10:00–10:15</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>10:15–10:30</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>10:30–11:00</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>11:00–12:00</b></td><td valign=top style="padding-top: 14px;"><b>Poster Session</b></td></tr>
| |
− | <tr><td valign=top width=100> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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>12:00–12:30</b></td><td valign=top style="padding-top: 14px;"><b>Session 2: Ontology and Typology</b></td></tr>
| |
− | <tr><td valign=top width=100>12:00–12:15</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>12:15–12:30</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>12:30–14:00</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>14:00–15:30</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>14:00–14:15</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>14:15–14:30</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>14:30–14:45</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>14:45–15:00</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>15:00–15:15</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>15:30–16:00</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>16:00–17:00</b></td><td valign=top style="padding-top: 14px;">Session 4: Shared Task</td></tr>
| |
− | <tr><td valign=top width=100>16:00–16:15</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>16:15–16:30</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>16:30–16:45</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>16:45–17:00</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>17:00–18:00</b></td><td valign=top style="padding-top: 14px;"><b>Shared Task Poster Session</b></td></tr>
| |
− | <tr><td valign=top width=100> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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> </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===
| |
− |
| |
− | * Hadi Amiri, Harvard Medical School, USA
| |
− | * Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK
| |
− | * Emilia Apostolova, Language.ai, USA
| |
− | * Eiji Aramaki, University of Tokyo, Japan
| |
− | * Asma Ben Abacha, US National Library of Medicine
| |
− | * Cosmin (Adi) Bejan, Vanderbilt University, Nashville, TN
| |
− | * Siamak Barzegar, Barcelona Supercomputing Center, Spain
| |
− | * Olivier Bodenreider, US National Library of Medicine
| |
− | * Leonardo Campillos Llanos, Universidad Autónoma de Madrid, Spain
| |
− | * Qingyu Chen, US National Library of Medicine
| |
− | * 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
| |
− | * Dina Demner-Fushman, US National Library of Medicine
| |
− | * 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
| |
− | * Sadid Hasan, Philips Research, Cambridge, MA
| |
− | * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia
| |
− | * Meizhi Ju, National Centre for Text Mining and University of Manchester, UK
| |
− | * Will Kearns, University of Washington, USA
| |
− | * Halil Kilicoglu, US National Library of Medicine
| |
− | * 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-Universität zu Berlin, Germany
| |
− | * Gal Levy-Fix, Columbia University, NY
| |
− | * Maolin Li, National Centre for Text Mining and University of Manchester, UK
| |
− | * Ramon Maldonado, The University of Texas at Dallas, USA
| |
− | * Timothy Miller, Children’s Hospital Boston, USA
| |
− | * 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
| |
− | * 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
| |
− | * 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
| |
− | * Max Savery, US National Library of Medicine
| |
− | * 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
| |
− | * 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===
| |
− | 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===
| |
− |
| |
− | The ACL BioNLP workshop associated with the SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in
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− | language processing for the biological and medical domains. The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP
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− | 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.
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− | The workshop will continue presenting work on a broad and interesting range of topics in NLP.
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− | The active areas of research include, but are not limited to:
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− | * Entity identification and normalization for a broad range of semantic categories
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− | * Extraction of complex relations and events
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− | * Semantic parsing
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− | * Discourse analysis
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− | * Anaphora /Coreference resolution
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− | * Text mining
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− | * Literature based discovery
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− | * Summarization
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− | * Question Answering
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− | * Resources and novel strategies for system testing and evaluation
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− | * Infrastructures for biomedical text mining
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− | * Processing and annotation platforms
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− | * Translating NLP research to practice
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− | * Research Reproducibility
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− | ===SUBMISSION INSTRUCTIONS===
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− | Three types of submissions are invited: full papers, short papers and MEDIQA shared task participants' reports.
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− | Full papers should not exceed eight (8) pages of text, plus unlimited references.
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− | 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.
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− | Full papers are intended to be reports of original research.
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− | 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.
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− | This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation.
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− | Both types of research and combinations thereof are encouraged.
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− | Short papers may consist of up to four (4) pages of content, plus unlimited references.
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− | Upon acceptance, short papers will still be given up to five (5) content pages in the proceedings.
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− | Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
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− | MEDIQA shared task participants reports should conform to the long paper submission guidelines.
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− | ====Electronic Submission====
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− | Submissions must be electronic and in PDF format, using the Softconf START conference management system at https://www.softconf.com/acl2019/bionlp/
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− | 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.”
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− | <b>Submissions need to be anonymous.</b>
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− | ====Dual submission policy====
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− | Papers may NOT be submitted to the BioNLP 2019 workshop if they are or will be concurrently submitted to another meeting or publication.
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| === MEDIQA 2019 === | | === MEDIQA 2019 === |