BioNLP 2023
BIONLP 2022 @ ACL 2022
The 21st BioNLP workshop associated with the ACL SIGBIOMED special interest group is co-located with ACL 2022
IMPORTANT DATES
- March 7, 2022: Workshop Paper Due Date
- Submission site: https://www.softconf.com/acl2022/BioNLP2022
- March 28, 2022: Notification of Acceptance
- April 10, 2022: Camera-ready papers due
- BioNLP 2022 Workshop at ACL, May 26, 2022, Dublin, Ireland
Submission Types & Requirements
Following the previous conferences, BioNLP 2022 will be open for two types of submissions: long and short papers. Please follow ACL guidelines https://acl-org.github.io/ACLPUB/formatting.html and templates: https://github.com/acl-org/acl-style-files
Overleaf templates: https://www.overleaf.com/project/5f64f1fb97c4c50001b60549
WORKSHOP OVERVIEW AND SCOPE
The BioNLP workshop associated with the ACL SIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains. Despite, or maybe due to reaching maturity, the field of Biomedical NLP continues getting stronger. BioNLP welcomes and encourages inclusion and diversity. BioNLP truly encompasses the breadth of the domain and brings together researchers in bio- and clinical NLP from all over the world. The workshop will continue presenting work on a broad and interesting range of topics in NLP.
BioNLP 2022 will be particularly interested in work on detection and mitigation of bias, BioNLP research in languages other than English, particularly, under-represented languages, and health disparities.
Other active areas of research include, but are not limited to:
- Entity identification and normalization (linking) for a broad range of semantic categories;
- Extraction of complex relations and events;
- Discourse analysis;
- Anaphora/coreference resolution;
- Text mining / Literature based discovery;
- Summarization;
- Τext simplification;
- Question Answering;
- Resources and strategies for system testing and evaluation;
- Infrastructures and pre-trained language models for biomedical NLP / Processing and annotation platforms;
- Development of synthetic data;
- Translating NLP research into practice;
- Getting reproducible results.
Program Committee
* Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK * Saadullah Amin, Saarland University, Germany * Emilia Apostolova, Anthem, Inc., USA * Eiji Aramaki, University of Tokyo, Japan * Kush Attal, US National Library of Medicine * Timothy Baldwin, University of Melbourne, Australia * Spandana Balumuri, National Institute of Technology Karnataka, India * Steven Bethard, University of Arizona, USA * Robert Bossy, Inrae, Université Paris Saclay, France * Berry de Bruijn, National Research Council Canada * Leonardo Campillos-Llanos, Centro Superior de Investigaciones Científicas - CSIC, Spain * Kevin Bretonnel Cohen, University of Colorado School of Medicine, USA * Qingyu Chen, US National Library of Medicine * Fenia Christopoulou, Huawei Noah's Ark lab, UK * Brian Connolly, Ohio, USA * Mike Conway, University of Utah, USA * Manirupa Das, Amazon, USA * Surabhi Datta, The University of Texas Health Science Center at Houston, USA * Dina Demner-Fushman, US National Library of Medicine * Dmitriy Dligach, Loyola University Chicago, USA * Kathleen C. Fraser, National Research Council Canada * Travis Goodwin, US National Library of Medicine * Natalia Grabar, CNRS, U Lille, France * Cyril Grouin, LIMSI - CNRS, France * Tudor Groza, EMBL-EBI * Deepak Gupta, US National Library of Medicine * Thierry Hamon, LIMSI-CNRS, France * Sam Henry, Christopher Newport University, USA * William Hogan, UCSD, USA * Emilee Holtzapple, University of Pittsburgh, USA * Kexin Huang, Stanford University, USA * Brian Hur, University of Melbourne, Australia * Richard Jackson, AstraZeneca * Antonio Jimeno Yepes, IBM, Melbourne Area, Australia * Sarvnaz Karimi, CSIRO, Australia * Nazmul Kazi, Montana State University, USA * Won Gyu KIM, US National Library of Medicine * Ari Klein, University of Pennsylvania, USA * Roman Klinger, University of Stuttgart, Germany * Andre Lamurias, Aalborg University, DK * Majid Latifi, National College of Ireland * Alberto Lavelli, FBK-ICT, Italy * Robert Leaman, US National Library of Medicine * Lung-Hao Lee, National Central University, Taiwan * Ulf Leser, Humboldt-Universität zu Berlin, Germany * Diwakar Mahajan, IBM Thomas J. Watson Research Center, USA * Khalil Mrini, University of California, San Diego, USA * Mark-Christoph Müller, Heidelberg Institute for Theoretical Studies, Germany * Claire Nédellec, INRA, Université Paris-Saclay, FR * Guenter Neumann, DFKI, Saarland, Germany * Aurelie Neveol, LIMSI - CNRS, France * Mariana Neves, Hasso-Plattner-Institute at the University of Potsdam, Germany * Denis Newman-Griffis, University of Pittsburgh, USA * Yifan Peng, Weill Cornell Medical College, USA * Francisco J. Ribadas-Pena, Universidade de Vigo, Spain * Anthony Rios, The University of Texas at San Antonio, USA * Angus Roberts, King's College London, UK * Kirk Roberts, The University of Texas Health Science Center at Houston, USA * Roland Roller, DFKI, Germany * Mourad Sarrouti, Sumitovant Biopharma, Inc., USA * Mario Sänger, Humboldt-Universität zu Berlin, Germany * Diana Sousa, Universidade de Lisboa, Portugal * Michael Spranger, Sony, Tokyo, Japan * Peng Su, University of Delaware, USA * Madhumita Sushil, University of California, San Francisco, USA * Karin Verspoor, RMIT University, Melbourne, Australia * Roger Wattenhofer, ETH Zurich, Switzerland * Leon Weber, Humboldt Universität Berlin, Germany * Nathan M. White, James Cook University, Australia * Davy Weissenbacher, University of Pennsylvania, USA * W John Wilbur, US National Library of Medicine * Amelie Wührl, University of Stuttgart, Germany * Dongfang Xu, Harvard University, USA * Shweta Yadav, University of Illinois Chicago, USA * Jingqing Zhang, Imperial College London, UK * Ayah Zirikly, he Johns Hopkins University, USA * Pierre Zweigenbaum, LIMSI - CNRS, France
SHARED TASK: MedVidQA 2022
The first challenge on Medical Video Question Answering is collocated with the BioNLP 2022 Workshop. MedVidQA focuses on providing relevant segments of videos as answers to health-related questions. Medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions. Please check the challenge website for details on the tasks, datasets, and submission guidelines: https://medvidqa.github.io
Organizers
Dina Demner-Fushman, US National Library of Medicine Kevin Bretonnel Cohen, University of Colorado School of Medicine Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK Jun-ichi Tsujii, National Institute of Advanced Industrial Science and Technology, Japan
Dual submission policy
Papers may NOT be submitted to the BioNLP 2022 workshop if they are or will be concurrently submitted to another meeting or publication.