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| { | |
| "text": "The International Workshop on Health Text Mining and Information Analysis (LOUHI) provides an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. The 10 previous editions of the workshop were co-located with SMBM 2008 in Turku, Finland, with NAACL 2010 in Los Angeles, California, with Artificial Intelligence in Medicine (AIME 2011) in Bled, Slovenia, during NICTA Techfest 2013 in Sydney, Australia, co-located with EACL 2014 in Gothenburg, Sweden, with EMNLP 2015 in Lisbon, Portugal, with EMNLP 2016 in Austin, Texas; in 2017 was held in Sydney, Australia; in 2018 was co-located with EMNLP 2018 in Brussels, Belgium; and in 2019 was co-located with EMNLP 2019 in Hong Kong. This year the workshop is co-located with EMNLP 2020 and takes place online due to the COVID-19 pandemics.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The aim of the LOUHI 2020 workshop is to bring together research work on topics related to health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. The topics include, but are not limited to, the following Natural Language Processing techniques and related areas:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Techniques supporting information extraction, e.g. named entity recognition, negation and uncertainty detection", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Text representation, including dealing with data sparsity and dimensionality issues", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Domain adaptation, e.g. adaptation of standard NLP tools (incl. tokenizers, PoS-taggers, etc) to the medical domain", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Information fusion, i.e. integrating data from various sources, e.g. structured and narrative documentation", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Unsupervised methods, including distributional semantics", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Evaluation, gold/reference standard construction and annotation", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Syntactic, semantic and pragmatic analysis of health documents", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Anonymization/de-identification of health records and ethics", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Supporting the development of medical terminologies and ontologies", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Individualization of content, consumer health vocabularies, summarization and simplification of text", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 NLP for supporting documentation and decision making practices", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infections", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Terminology and information model standards (SNOMED CT, FHIR) for health text mining", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Bridging gaps between formal ontology and biomedical NLP The call for papers encouraged authors to submit papers describing substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress. We also encouraged to report work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This year we received a high number of submissions (43), therefore the selection process was very competitive. Due to time and space limitations, we could only choose a small number of the submitted papers to appear in the program.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Each submission went through a double-blind review process which involved three program committee members. Based on comments and rankings supplied by the reviewers, we accepted 16 papers. Although the selection was entirely based on the scores provided by the reviewers, we regretfully had to set a relatively high threshold for acceptance. The overall acceptance rate is 37%.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Our special thanks go to Guergana Savova for accepting to give an invited talk.", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Finally, we would like to thank the members of the program committee for providing balanced reviews in a very short period of time, and the authors for their submissions and the quality of their work.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "November 20, 2020(continued) xii", | |
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| "raw_text": "Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating Minghao Zhu, Youzhe Song, Ge Jin and Keyuan Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127", | |
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| "raw_text": "November 20, 2020 (continued) 10:45-11:00 Break 11:00-12:20 Session 2 11:00 Normalization of Long-tail Adverse Drug Reactions in Social Media Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Robert-Jan Sips, Zoltan Szlanik and Selene Baez 11:15 Evaluation of Machine Translation Methods applied to Medical Terminologies Konstantinos Skianis, Yann Briand and Florent Desgrippes 11:30 Information retrieval for animal disease surveillance: a pattern-based approach. Sarah Valentin, Mathieu Roche and Renaud Lancelot 11:45 Multitask Learning of Negation and Speculation using Transformers Aditya Khandelwal and Benita Kathleen Britto 12:00 Session 2 QA 12:20-14:00 Break 14:00-14:45 Invited Talk 14:00 TBA Guergana Savova 14:30 Invited Talk QA x November 20, 2020 (continued) 14:45-15:00 Break 15:00-16:15 Session 3 15:00 Biomedical Event Extraction as Multi-turn Question Answering Xing David Wang, Leon Weber and Ulf Leser 15:15 An efficient representation of chronological events in medical texts Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, Hao Ni, Goran Nenadic and Alejo Nevado-Holgado 15:25 Defining and Learning Refined Temporal Relations in the Clinical Narrative Kristin Wright-Bettner, Chen Lin, Timothy Miller, Steven Bethard, Dmitriy Dli- gach, Martha Palmer, James H. Martin and Guergana Savova 15:40 Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains Tarek Sakakini, Jong Yoon Lee, Aditya Duri, Renato F.L. Azevedo, Victor Sadauskas, Kuangxiao Gu, Suma Bhat, Dan Morrow, James Graumlich, Saqib Walayat, Mark Hasegawa-Johnson, Thomas Huang, Ann Willemsen-Dunlap and Donald Halpin 15:55 Session 3 QA 16:15-16:30 Break 16:30-17:30 Session 4 16:30 Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating Minghao Zhu, Youzhe Song, Ge Jin and Keyuan Jiang 16:45 Detecting Foodborne Illness Complaints in Multiple Languages Using English An- notations Only Ziyi Liu, Giannis Karamanolakis, Daniel Hsu and Luis Gravano 17:00 Detection of Mental Health from Reddit via Deep Contextualized Representations Zhengping Jiang, Sarah Ita Levitan, Jonathan Zomick and Julia Hirschberg 17:15 Session 4 QA", | |
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