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| "text": "BioNLP 2020: Research unscathed by Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii", | |
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| "text": "The past year has been more than exciting for natural language processing in general, and for biomedical natural language processing in particular. A gradual accretion of studies of reproducibility and replicability in natural language processing, biomedical and otherwise had been making it clear that the reproducibility crisis that has hit most of the rest of science is not going to spare text mining or its related fields. Then, in March of 2020, much of the world ground to a sudden halt.", | |
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| "text": "The outbreak of the COVID-19 disease caused by the novel coronavirus SARS-CoV-2 made computational work more obviously relevant than it had perhaps ever been before. Suddenly, newscasters were arguing about viral clades, the daily news was full of stories about modelling, and your neighbor had heard of PCR. But, some of us did not really see a role for natural language processing in the brave new world of computational instant reactions to an international pandemic.", | |
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| "text": "That was wrong.", | |
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| "text": "In mid-late March of 2020, a joint project between the Allen Artificial Intelligence Institute (Ai2), the National Library of Medicine (NLM), and the White House Office of Science and Technology Policy (OSTP) released CORD-19, a corpus of work on the SARS-CoV-2 virus, on COVID-19 disease, and on related coronavirus research. It was immediately notable for its inclusion of \"gray literature\" from preprint servers, which mostly have been neglected in text mining research, as well as for its flexibility with regards to licensing of content types. Perhaps most importantly, it was released in conjunction with a number of task types, including one related to ethics-although the value of medical ethics has been widely obvious since the Nazi \"medical\" experimentation horrors of the Second World War, the worldwide pandemic has made the value of medical ethicists more apparent to the general public than at any time since. Those task type definitions enabled the broader natural language processing community to jump into the fray quite quickly, and initial results have been quick to arrive.", | |
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| "text": "Meanwhile, the pandemic did nothing to slow research in biomedical natural language processing on any other topic, either. That can be seen in the fact that this year the Association for Computational Linguistics SIGBIOMED workshop on biomedical natural language processing received 73 submissions. The unfortunate effect of the pandemic was the cancellation of the physical workshop, which would have allowed acceptance of all high-quality submissions as posters, if not for podium presentations. Indeed, the poster sessions at BioNLP have been continuously growing in size, due to the large number of high-quality submissions that the workshop receives annually. Unfortunately, because this year the Association for Computational Linguistics annual meeting will take place online only, there will be no poster session for the workshop. Consequently, only a handful of submissions could be accepted for presentation.", | |
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| "text": "Transitioning of the traditional conferences to online presentations at the beginning of the COVID-19 pandemic showed that the traditional presentation formats are not as engaging remotely as they are in the context of in-person sessions. We are therefore exploring a new form of presentation, hoping it will be more engaging, interactive, and informative: 22 papers (about 30% of the submissions) will be presented in panel-like sessions. Papers will be grouped by similarity of topic, meaning that participants with related interests will be able to interact regarding their papers with a hopefully optimal number of people on line at the same time. As we write this introduction, the conference plans and platform are still evolving, as are the daily lives of much of the planet, so we hope that you will join us in planning for the worst, while hoping for the best. Presentations: Bringing clinical problems and poetry together, this creative work seeks to better understand dyslexia through a self-attention transformer and Shakespearean sonnets (Bleiweiss). Detection of early stages of Alzheimer's disease using unsupervised clustering is explored with 10 years of President Ronald Reagan's speeches (Wang et al.) . Stavropoulos et al. introduce BIOMRC, a clozestyle dataset for biomedical machine reading comprehension, along with new publicly available models, and provide a leaderboard for the task. Another type of question answering -answering questions that can be answered by electronic medical records-is explored by Rawat et al. Hur et al. study veterinary records to identify reasons for administration of antibiotics. DeYoung et al. expand the Evidence Inference dataset and evaluate BERT-based models for the evidence inference task.", | |
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| "start": 1207, | |
| "end": 1220, | |
| "text": "(Wang et al.)", | |
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| { | |
| "text": "Invited talk and discussion lead: Hoifung Poon", | |
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| "section": "Session 4: Named Entity Recognition and Knowledge Representation", | |
| "sec_num": null | |
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| "text": "The advent of big data promises to revolutionize medicine by making it more personalized and effective, but big data also presents a grand challenge of information overload. For example, tumor sequencing has become routine in cancer treatment, yet interpreting the genomic data requires painstakingly curating knowledge from a vast biomedical literature, which grows by thousands of papers every day. Electronic medical records contain valuable information to speed up clinical trial recruitment and drug development, but curating such real-world evidence from clinical notes can take hours for a single patient. Natural language processing (NLP) can play a key role in interpreting big data for precision medicine. In particular, machine reading can help unlock knowledge from text by substantially improving curation efficiency. However, standard supervised methods require labeled examples, which are expensive and time-consuming to produce at scale. In this talk, Dr. Poon presents Project Hanover, where the team overcomes the annotation bottleneck by combining deep learning with probabilistic logic, and by exploiting self-supervision from readily available resources such as ontologies and databases. This enables the researchers to extract knowledge from millions of publications, reason efficiently with the resulting knowledge graph by learning neural embeddings of biomedical entities and relations, and apply the extracted knowledge and learned embeddings to supporting precision oncology. ", | |
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| "section": "Invited talk: Machine Reading for Precision Medicine", | |
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| "text": "As always, we are profoundly grateful to the authors who chose BioNLP for presenting their innovative research. The authors' willingness to continue sharing their work through BioNLP consistently makes the workshop noteworthy and stimulating. We are equally indebted to the program committee members (listed elsewhere in this volume) who produced thorough reviews on a tight review schedule and with an admirable level of insight, despite the timeline being even shorter than usual and the workload higher, while at the same time handling the unprecedented changes in their work and life caused by the COVID-19 pandemic.", | |
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| "section": "Acknowledging the community", | |
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| "raw_text": "Thursday July 9, 2020 08:30-08:40 Opening remarks 08:40-10:30 Session 1: High accuracy information retrieval, spin and bias 08:40-09:10 Invited Talk -Kirk Roberts 09:10-09:20 Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embed- dings Anthony Rios, Reenam Joshi and Hejin Shin 09:20-09:30 Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Auto- mated Text Categorization in Biomedical IR using Self-Attention Manirupa Das, Juanxi Li, Eric Fosler-Lussier, Simon Lin, Steve Rust, Yungui Huang and Rajiv Ramnath 09:30-09:40 Interactive Extractive Search over Biomedical Corpora Hillel Taub Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen and Yoav Goldberg 09:40-09:50 Improving Biomedical Analogical Retrieval with Embedding of Structural Depen- dencies Amandalynne Paullada, Bethany Percha and Trevor Cohen 09:50-10:00 DeSpin: a prototype system for detecting spin in biomedical publications Anna Koroleva, Sanjay Kamath, Patrick Bossuyt and Patrick Paroubek 10:00-10:30 Discussion 10:30-10:45 Coffee Break Thursday July 9, 2020 (continued) 10:45-13:00 Session 2: Clinical Language Processing 10:45-11:15 Invited Talk -Tim Miller 11:15-11:25 Towards Visual Dialog for Radiology Olga Kovaleva, Chaitanya Shivade, Satyananda Kashyap, Karina Kanjaria, Joy Wu, Deddeh Ballah, Adam Coy, Alexandros Karargyris, Yufan Guo, David Beymer Beymer, Anna Rumshisky and Vandana Mukherjee Mukherjee 11:25-11:35 A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extrac- tion Chen Lin, Timothy Miller, Dmitriy Dligach, Farig Sadeque, Steven Bethard and Guergana Savova 11:35-11:45 Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset Thomas Searle, Zina Ibrahim and Richard Dobson 11:45-11:55 Comparative Analysis of Text Classification Approaches in Electronic Health Records Aurelie Mascio, Zeljko Kraljevic, Daniel Bean, Richard Dobson, Robert Stewart, Rebecca Bendayan and Angus Roberts 11:55-12:05 Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning Liyan Xu, Julien Hogan, Rachel E. Patzer and Jinho D. Choi 12:05-12:15 Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity Yuxia Wang, Fei Liu, Karin Verspoor and Timothy Baldwin 12:15-12:45 Discussion 12:45-13:30 Lunch Thursday July 9, 2020 (continued) 13:30-15:30 Session 3: Language Understanding 13:30-14:00 Invited Talk -Anna Rumshisky 14:00-14:10 Entity-Enriched Neural Models for Clinical Question Answering Bhanu Pratap Singh Rawat, Wei-Hung Weng, So Yeon Min, Preethi Raghavan and Peter Szolovits 14:10-14:20 Evidence Inference 2.0: More Data, Better Models Jay DeYoung, Eric Lehman, Benjamin Nye, Iain Marshall and Byron C. Wallace 14:20-14:30 Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches Ning Wang, Fan Luo, Vishal Peddagangireddy, Koduvayur Subbalakshmi and Ra- jarathnam Chandramouli 14:30-14:40 BioMRC: A Dataset for Biomedical Machine Reading Comprehension Dimitris Pappas, Petros Stavropoulos, Ion Androutsopoulos and Ryan McDonald 14:40-14:50 Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Repre- sentation Avi Bleiweiss 14:50-15:00 Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt and James Gilker- son 15:00-15:30 Discussion 15:30-15:45 Coffee Break Thursday July 9, 2020 (continued) 15:45-17:45 Session 4: Named Entity Recognition and Knowledge Representation 15:45-16:25 Invited Talk: Machine Reading for Precision Medicine, Hoifung Poon 16:25-16:35 Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings David Chang, Ivana Bala\u017eevi\u0107, Carl Allen, Daniel Chawla, Cynthia Brandt and Andrew Taylor 16:35-16:45 Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience Isar Nejadgholi, Kathleen C. Fraser and Berry de Bruijn 16:45-16:55 A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction Saadullah Amin, Katherine Ann Dunfield, Anna Vechkaeva and Guenter Neumann 16:55-17:05 Global Locality in Biomedical Relation and Event Extraction Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong and David Martinez Iraola 17:05-17:15 An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining Yifan Peng, Qingyu Chen and Zhiyong Lu 17:15-17:45 Discussion 17:45-18:00 Closing remarks", | |
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| "text": "papers referenced in this section are included in this volume, unless otherwise indicated Session 1: High accuracy information retrieval, spin and bias Invited talk and discussion lead: Kirk RobertsPresentations: The exploration of Information Retrieval approaches enhanced with linguistic knowledge continues in the work that allows life-science researchers to search PubMed and the CORD-19 collection using patterns over dependency graphs(Taub-Tabib et al.) Representing biomedical relationships in the literature by encoding dependency structure with word embeddings promises to improve retrieval of relationships and literature-based discovery(Paullada et al.) Word embeddings trained on biomedical research articles and the tests based on their associations and coherence, among others, allow detecting and quantifying gender bias over time (Rios et al.) A BioBERT model fine-tuned for relation extraction might assist in detecting spin in reporting the results of randomized clinical trials (Koroleva et al.) Finally, a novel sequence-to-set approach to generating terms for pseudo-relevance feedback is evaluated (Das et al.) Session 2: Clinical Language Processing Invited talk and discussion lead: Tim Miller Presentations: Not surprisingly, much of the potentially reproducible work in the clinical domain is based on the Medical Information Mart for Intensive Care (MIMIC) data (Johnson et al., 2016). Kovaleva et al. used the MIMIC-CXR data to explore Visual Dialog for radiology and prepare the first publicly available silver-and gold-standard datasets for this task. Searle et al. present a MIMIC-based silver standard for automatic clinical coding and warn that frequently assigned codes in MIMIC-III might be undercoded. Mascio et al. used MIMIC and the Shared Annotated Resources (ShARe)/CLEF dataset in four classification tasks: disease status, temporality, negation, and uncertainty. Temporality is explored in-depth by Lin et al., and Wang et al. explore approaches to a clinical Semantic Textual Similarity (STS) task. Xu et al. apply reinforcement learning to deal with noise in clinical text for readmission prediction after kidney transplant. Session 3: Language Understanding Invited talk and discussion lead: Anna Rumshisky" | |
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| "text": "Poon is the Senior Director of Precision Health NLP at Microsoft Research and an affiliated professor at the University of Washington Medical School. He leads Project Hanover, with the overarching goal of structuring medical data for precision medicine. He has given tutorials on this topic at top conferences such as the Association for Computational Linguistics (ACL) and the Association for the Advancement of Artificial Intelligence (AAAI). His research spans a wide range of problems in machine learning and natural language processing (NLP), and his prior work has been recognized with Best Paper Awards from premier venues such as the North American Chapter of the Association for Computational Linguistics (NAACL), Empirical Methods in Natural Language Processing (EMNLP), and Uncertainty in AI (UAI). He received his PhD in Computer Science and Engineering from University of Washington, specializing in machine learning and NLP.Presentations: Nejadgholi et al. analyze errors in NER and introduce an F-score that models a forgiving user experience. Peng et al. study NER, relation extraction, and other tasks with a multi-tasking learning approach. Amin et al. explore multi-instance learning for relation extraction. ShafieiBavani et al. also explore relation and event extraction, but in the context of simultaneously predicting relationships between all mention pairs in a text. Chang et al. provide a benchmark for knowledge graph embedding models on the SNOMED-CT knowledge graph and emphasize the importance of knowledge graphs for learning biomedical knowledge representation." | |
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