Difference between revisions of "BioNLP 2023"
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* Yassine M'Rabet, US National Library of Medicine | * Yassine M'Rabet, US National Library of Medicine | ||
* Aurelie Neveol, LIMSI - CNRS, France | * Aurelie Neveol, LIMSI - CNRS, France | ||
| − | + | * Claire Nédellec, INRA, France | |
* Mariana Neves, German Federal Institute for Risk Assessment, Germany | * Mariana Neves, German Federal Institute for Risk Assessment, Germany | ||
* Denis Newman-Griffis, Clinical Center, National Institutes of Health, USA | * Denis Newman-Griffis, Clinical Center, National Institutes of Health, USA | ||
Revision as of 13:59, 9 August 2019
BIONLP 2019
Florence, Italy, Thursday, August 1, 2019
An ACL 2019 Workshop associated with the SIGBIOMED special interest group and featuring an associated task: MEDIQA 2019 ( https://sites.google.com/view/mediqa2019)
IMPORTANT DATES
- Submission deadline: Friday May 10, 2019 11:59 PM Eastern US
- Notification of acceptance: Friday, May 31, 2019
- Camera-ready copy due from authors: Friday, June 7, 2019 -- Firm deadline due to ACL schedule.
- Workshop: Thursday, August 1, 2019
BioNLP 2019 WORKSHOP PROGRAM
| Thursday August 1, 2019 | |
| 8:30–8:45 | Opening remarks |
| 8:45–10:30 | Session 1: Clinical and Translational NLP |
| 8:45–9:00 | Classifying the reported ability in clinical mobility descriptions Denis Newman-Griffis, Ayah Zirikly, Guy Divita, Bart Desmet |
| 9:00–9:15 | Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes Steven Kester Yuwono, Hwee Tou Ng, Kee Yuan Ngiam |
| 9:15–9:30 | A Paraphrase Generation System for EHR Question Answering Sarvesh Soni and Kirk Roberts |
| 9:30–9:45 | REflex: Flexible Framework for Relation Extraction in Multiple Domains Geeticka Chauhan, Matthew McDermott, Peter Szolovits |
| 9:45–10:00 | Analysing Representations of Memory Impairment in a Clinical Notes Classification Model Mark Ormerod, Jesús Martínez-del-Rincón, Neil Robertson, Bernadette McGuinness, Barry Devereux |
| 10:00–10:15 | Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets Yifan Peng, Shankai Yan, Zhiyong Lu |
| 10:15–10:30 | Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support Emilia Apostolova, Tony Wang, Tim Tschampel, Ioannis Koutroulis, Tom Velez |
| 10:30–11:00 | Coffee Break |
| 11:00–12:00 | Poster Session |
| MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language Maria Mitrofan, Verginica Barbu Mititelu, Grigorina Mitrofan | |
| Domain Adaptation of SRL Systems for Biological Processes Dheeraj Rajagopal, Nidhi Vyas, Aditya Siddhant, Anirudha Rayasam, Niket Tandon, Eduard Hovy | |
| Deep Contextualized Biomedical Abbreviation Expansion Qiao Jin, Jinling Liu, Xinghua Lu | |
| RNN Embeddings for Identifying Difficult to Understand Medical Words Hanna Pylieva, Artem Chernodub, Natalia Grabar, Thierry Hamon | |
| A distantly supervised dataset for automated data extraction from diagnostic studies Christopher Norman, Mariska Leeflang, René Spijker, Evangelos Kanoulas, Aurélie Névéol | |
| Query selection methods for automated corpora construction with a use case in food-drug interactions Georgeta Bordea, Tsanta Randriatsitohaina, Fleur Mougin, Natalia Grabar, Thierry Hamon | |
| Enhancing biomedical word embeddings by retrofitting to verb clusters Billy Chiu, Simon Baker, Martha Palmer, Anna Korhonen | |
| A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre | |
| Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns Julien Fauqueur, Ashok Thillaisundaram, Theodosia Togia | |
| First Steps towards Building a Medical Lexicon for Spanish with Linguistic and Semantic Information Leonardo Campillos-Llanos | |
| Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing Xindi Wang and Robert E. Mercer | |
| BioRelEx 1.0: Biological Relation Extraction Benchmark Hrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, Aram Galstyan | |
| Extraction of Lactation Frames from Drug Labels and LactMed Heath Goodrum, Meghana Gudala, Ankita Misra, Kirk Roberts | |
| Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions Natalia Viani, Hegler Tissot, Ariane Bernardino, Sumithra Velupillai | |
| Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms Leonie Grön, Ann Bertels, Kris Heylen | |
| Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding Hichem Mezaoui, Isuru Gunasekara, Aleksandr Gontcharov | |
| Contributions to Clinical Named Entity Recognition in Portuguese Fábio Lopes, César Teixeira, Hugo Gonçalo Oliveira | |
| Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives? Zhaodong Yan, Serena Jeblee, Graeme Hirst | |
| Is artificial data useful for biomedical Natural Language Processing algorithms? Zixu Wang, Julia Ive, Sumithra Velupillai, Lucia Specia | |
| ChiMed: A Chinese Medical Corpus for Question Answering Yuanhe Tian, Weicheng Ma, Fei Xia, Yan Song | |
| Clinical Concept Extraction for Document-Level Coding Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, Jacob Eisenstein | |
| Clinical Case Reports for NLP Cyril Grouin, Natalia Grabar, Vincent Claveau, Thierry Hamon | |
| Two-stage Federated Phenotyping and Patient Representation Learning Dianbo Liu, Dmitriy Dligach, Timothy Miller | |
| Transfer Learning for Causal Sentence Detection Manolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev, Joan Ginés i Ametllé | |
| 12:00–12:30 | Session 2: Ontology and Typology |
| 12:00–12:15 | Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan McDonald, Marianna Apidianaki |
| 12:15–12:30 | Simplification-induced transformations: typology and some characteristics Anaïs Koptient, Rémi Cardon, Natalia Grabar |
| 12:30–14:00 | Lunch break |
| 14:00–15:30 | Session 3: Literature mining approaches and models |
| 14:00–14:15 | ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar |
| 14:15–14:30 | Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings Zenan Zhai, Dat Quoc Nguyen, Saber Akhondi, Camilo Thorne, Christian Druckenbrodt, Trevor Cohn, Michelle Gregory, Karin Verspoor |
| 14:30–14:45 | Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning Hassan Alhuzali and Sophia Ananiadou |
| 14:45–15:00 | Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations Gaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov, Roland Roller |
| 15:00–15:15 | Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications Anna Koroleva and Patrick Paroubek |
| 15:30–16:00 | Coffee Break |
| 16:00–17:00 | Session 4: Shared Task |
| 16:00–16:15 | Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering Asma Ben Abacha, Chaitanya Shivade and Dina Demner-Fushman |
| 16:15–16:30 | PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation Wei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni and Guotong Xie |
| 16:30–16:45 | Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment Hemant Pugaliya, Karan Saxena, Shefali Garg, Sheetal Shalini, Prashant Gupta, Eric Nyberg and Teruko Mitamura |
| 16:45–17:00 | DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain Yichong Xu, Xiaodong Liu, Chunyuan Li, Hoifung Poon and Jianfeng Gao |
| 17:00–18:00 | Shared Task Poster Session |
| Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model Jiin Nam, Seunghyun Yoon and Kyomin Jung | |
| WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian and Fei Xia | |
| KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI Cemil Cengiz, Ulaş Sert and Deniz Yuret | |
| DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering Huiwei Zhou, Xuefei Li, Weihong Yao, Chengkun Lang and Shixian Ning | |
| DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering Huiwei Zhou, Bizun Lei, Zhe Liu and Zhuang Liu | |
| Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura and Eric Nyberg | |
| Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg and Teruko Mitamura | |
| IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier Prakhar Sharma and Sumegh Roychowdhury | |
| ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge Vincent Nguyen, Sarvnaz Karimi and Zhenchang Xing | |
| MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain. Sahil Chopra, Ankita Gupta and Anupama Kaushik | |
| UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain Noha Tawfik and Marco Spruit | |
| UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference William Kearns, Wilson Lau and Jason Thomas | |
| Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference Kamal raj Kanakarajan, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana, Soham Chatterjee and Malaikannan Sankarasubbu | |
| IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering Dibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh and Asif Ekbal | |
| LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition Andre Lamurias and Francisco M Couto | |
| NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model Lung-Hao Lee, Yi Lu, Po-Han Chen, Po-Lei Lee and Kuo-Kai Shyu | |
| ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System Anumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi, Sowmya Kamath and Anand Kumar | |
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 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 for a broad range of semantic categories
- Extraction of complex relations and events
- Semantic parsing
- Discourse analysis
- Anaphora /Coreference resolution
- Text mining
- Literature based discovery
- Summarization
- Question Answering
- Resources and novel strategies for system testing and evaluation
- Infrastructures for biomedical text mining
- Processing and annotation platforms
- Translating NLP research to practice
- Research Reproducibility
SUBMISSION INSTRUCTIONS
Three types of submissions are invited: full papers, short papers and MEDIQA shared task participants' reports.
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.
MEDIQA shared task participants reports should conform to the long paper submission guidelines.
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.”
Submissions need to be anonymous.
Dual submission policy
Papers may NOT be submitted to the BioNLP 2019 workshop if they are or will be concurrently submitted to another meeting or publication.
MEDIQA 2019
A BioNLP-19 shared task on textual inference and question entailment
In 2019, the workshop will present the results of the shared task on biomedical textual inference and question entailment. See details at https://sites.google.com/view/mediqa2019