Table of Contents

• Classifying the reported ability in clinical mobility descriptionsDenis Newman-Griffis, Ayah Zirikly, Guy Divita, Bart Desmet. 1-10 [doi]
• Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical NotesSteven Kester Yuwono, Hwee Tou Ng, Kee Yuan Ngiam. 11-19 [doi]
• A Paraphrase Generation System for EHR Question AnsweringSarvesh Soni, Kirk Roberts. 20-29 [doi]
• REflex: Flexible Framework for Relation Extraction in Multiple DomainsGeeticka Chauhan, Matthew B. A. McDermott, Peter Szolovits. 30-47 [doi]
• Analysing Representations of Memory Impairment in a Clinical Notes Classification ModelMark Ormerod, Jesús Martínez del Rincón, Neil Robertson, Bernadette McGuinness, Barry Devereux. 48-57 [doi]
• Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking DatasetsYifan Peng, Shankai Yan, Zhiyong Lu. 58-65 [doi]
• Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision SupportEmilia Apostolova, Tony Wang, Tim Tschampel, Ioannis Koutroulis, Tom Velez. 66-70 [doi]
• MoNERo: a Biomedical Gold Standard Corpus for the Romanian LanguageMaria Mitrofan, Verginica Barbu Mititelu, Grigorina Mitrofan. 71-79 [doi]
• Domain Adaptation of SRL Systems for Biological ProcessesDheeraj Rajagopal, Nidhi Vyas, Aditya Siddhant, Anirudha Rayasam, Niket Tandon, Eduard H. Hovy. 80-87 [doi]
• Deep Contextualized Biomedical Abbreviation ExpansionQiao Jin, Jinling Liu, Xinghua Lu. 88-96 [doi]
• RNN Embeddings for Identifying Difficult to Understand Medical WordsHanna Pylieva, Artem N. Chernodub, Natalia Grabar, Thierry Hamon. 97-104 [doi]
• A distantly supervised dataset for automated data extraction from diagnostic studiesChristopher Norman, Mariska Leeflang, René Spijker, Evangelos Kanoulas, Aurélie Névéol. 105-114 [doi]
• Query selection methods for automated corpora construction with a use case in food-drug interactionsGeorgeta Bordea, Tsanta Randriatsitohaina, Fleur Mougin, Natalia Grabar, Thierry Hamon. 115-124 [doi]
• Enhancing biomedical word embeddings by retrofitting to verb clustersBilly Chiu, Simon Baker, Martha Palmer, Anna Korhonen. 125-134 [doi]
• A Comparison of Word-based and Context-based Representations for Classification Problems in Health InformaticsAditya Joshi, Sarvnaz Karimi, Ross Sparks, Cécile Paris, C. Raina MacIntyre. 135-141 [doi]
• Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patternsJulien Fauqueur, Ashok Thillaisundaram, Theodosia Togia. 142-151 [doi]
• First Steps towards Building a Medical Lexicon for Spanish with Linguistic and Semantic InformationLeonardo Campillos Llanos. 152-164 [doi]
• Incorporating Figure Captions and Descriptive Text in MeSH Term IndexingXindi Wang, Robert E. Mercer. 165-175 [doi]
• BioRelEx 1.0: Biological Relation Extraction BenchmarkHrant Khachatrian, Lilit Nersisyan, Karen Hambardzumyan, Tigran Galstyan, Anna Hakobyan, Arsen Arakelyan, Andrey Rzhetsky, Aram Galstyan. 176-190 [doi]
• Extraction of Lactation Frames from Drug Labels and LactMedHeath Goodrum, Meghana Gudala, Ankita Misra, Kirk Roberts. 191-200 [doi]
• Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar ExpressionsNatalia Viani, Hegler Tissot, Ariane Bernardino, Sumithra Velupillai. 201-210 [doi]
• Leveraging Sublanguage Features for the Semantic Categorization of Clinical TermsLeonie Grön, Ann Bertels, Kris Heylen. 211-216 [doi]
• Enhancing PIO Element Detection in Medical Text Using Contextualized EmbeddingHichem Mezaoui, Isuru Gunasekara, Aleksandr Gontcharov. 217-222 [doi]
• Contributions to Clinical Named Entity Recognition in PortugueseFábio Lopes, César Teixeira, Hugo Gonçalo Oliveira. 223-233 [doi]
• Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?Zhaodong Yan, Serena Jeblee, Graeme Hirst. 234-239 [doi]
• Is artificial data useful for biomedical Natural Language Processing algorithms?Zixu Wang, Julia Ive, Sumithra Velupillai, Lucia Specia. 240-249 [doi]
• ChiMed: A Chinese Medical Corpus for Question AnsweringYuanhe Tian, Weicheng Ma, Fei Xia, Yan Song. 250-260 [doi]
• Clinical Concept Extraction for Document-Level CodingSarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, Jacob Eisenstein. 261-272 [doi]
• Clinical Case Reports for NLPCyril Grouin, Natalia Grabar, Vincent Claveau, Thierry Hamon. 273-282 [doi]
• Two-stage Federated Phenotyping and Patient Representation LearningDianbo Liu, Dmitriy Dligach, Timothy A. Miller. 283-291 [doi]
• Transfer Learning for Causal Sentence DetectionManolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev, Joan Ginés i Ametllé. 292-297 [doi]
• Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsSotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan Mcdonald, Marianna Apidianaki. 298-308 [doi]
• Simplification-induced transformations: typology and some characteristicsAnaïs Koptient, Rémi Cardon, Natalia Grabar. 309-318 [doi]
• ScispaCy: Fast and Robust Models for Biomedical Natural Language ProcessingMark Neumann, Daniel King, Iz Beltagy, Waleed Ammar. 319-327 [doi]
• Improving Chemical Named Entity Recognition in Patents with Contextualized Word EmbeddingsZenan Zhai, Dat Quoc Nguyen, Saber A. Akhondi, Camilo Thorne, Christian Druckenbrodt, Trevor Cohn, Michelle Gregory, Karin Verspoor. 328-338 [doi]
• Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer LearningHassan Alhuzali, Sophia Ananiadou. 339-347 [doi]
• Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept RepresentationsGaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov, Roland Roller. 348-358 [doi]
• Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publicationsAnna Koroleva, Patrick Paroubek. 359-369 [doi]
• Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question AnsweringAsma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman. 370-379 [doi]
• PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge DistillationWei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni, Guotong Xie. 380-388 [doi]
• Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question EntailmentHemant Pugaliya, Karan Saxena, Shefali Garg, Sheetal Shalini, Prashant Gupta, Eric Nyberg, Teruko Mitamura. 389-398 [doi]
• DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical DomainYichong Xu, Xiaodong Liu, Chunyuan Li, Hoifung Poon, Jianfeng Gao. 399-405 [doi]
• Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language ModelJiin Nam, Seunghyun Yoon 0002, Kyomin Jung. 406-414 [doi]
• WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language InferenceZhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian, Fei Xia. 415-426 [doi]
• KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLICemil Cengiz, Ulas Sert, Deniz Yuret. 427-436 [doi]
• DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question AnsweringHuiwei Zhou, Xuefei Li, Weihong Yao, Chengkun Lang, Shixian Ning. 437-445 [doi]
• DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question AnsweringHuiwei Zhou, Bizun Lei, Zhe Liu, Zhuang Liu 0001. 446-452 [doi]
• Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representationsVinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura, Eric Nyberg. 453-461 [doi]
• Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and EntailmentSai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg, Teruko Mitamura. 462-470 [doi]
• IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting ClassifierPrakhar Sharma, Sumegh Roychowdhury. 471-477 [doi]
• ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual KnowledgeVincent Nguyen, Sarvnaz Karimi, Zhenchang Xing. 478-487 [doi]
• MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical DomainSahil Chopra, Ankita Gupta, Anupama Kaushik. 488-492 [doi]
• UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical DomainNoha S. Tawfik, Marco Spruit. 493-499 [doi]
• UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language InferenceWilliam R. Kearns, Wilson Lau, Jason A. Thomas. 500-509 [doi]
• Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language InferenceKamal Raj Kanakarajan, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana, Soham Chatterjee, Malaikannan Sankarasubbu. 510-516 [doi]
• IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question AnsweringDibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh, Asif Ekbal. 517-522 [doi]
• LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity RecognitionAndre Lamurias, Francisco M. Couto. 523-527 [doi]
• NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention ModelLung-Hao Lee, Yi Lu, Po-Han Chen, Po-Lei Lee, Kuo-Kai Shyu. 528-532 [doi]
• ARS_NITK at MEDIQA 2019: Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering SystemAnumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi, Sowmya Kamath, Anand Kumar. 533-540 [doi]