Papers
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary et al.
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
Huiwei Zhou, Bizun Lei, Zhe Liu et al.
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 et al.
KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI
Cemil Cengiz, Ulaş Sert, Deniz Yuret
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Zhaofeng Wu, Yan Song, Sicong Huang et al.
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
Jiin Nam, Seunghyun Yoon, Kyomin Jung
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
Yichong Xu, Xiaodong Liu, Chunyuan Li et al.
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 et al.
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation
Wei Zhu, Xiaofeng Zhou, Keqiang Wang et al.
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications
Anna Koroleva, Patrick Paroubek
Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations
Gaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov et al.
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning
Hassan Alhuzali, Sophia Ananiadou
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
Zenan Zhai, Dat Quoc Nguyen, Saber Akhondi et al.
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Mark Neumann, Daniel King, Iz Beltagy et al.
Simplification-induced transformations: typology and some characteristics
Anaïs Koptient, Rémi Cardon, Natalia Grabar
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos et al.
Transfer Learning for Causal Sentence Detection
Manolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev et al.
Two-stage Federated Phenotyping and Patient Representation Learning
Dianbo Liu, Dmitriy Dligach, Timothy Miller
Clinical Case Reports for NLP
Cyril Grouin, Natalia Grabar, Vincent Claveau et al.
Clinical Concept Extraction for Document-Level Coding
Sarah Wiegreffe, Edward Choi, Sherry Yan et al.
ChiMed: A Chinese Medical Corpus for Question Answering
Yuanhe Tian, Weicheng Ma, Fei Xia et al.
Is artificial data useful for biomedical Natural Language Processing algorithms?
Zixu Wang, Julia Ive, Sumithra Velupillai et al.
Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?
Zhaodong Yan, Serena Jeblee, Graeme Hirst
Contributions to Clinical Named Entity Recognition in Portuguese
Fábio Lopes, César Teixeira, Hugo Gonçalo Oliveira