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ORCID4th SMM4H@ACL 2019: Florence, Italy
- Davy Weissenbacher, Graciela Gonzalez-Hernandez:
Proceedings of the Fourth Social Media Mining for Health Application Workshop & Shared Task, SMM4H@ACL 2019, Florence, Italy, August 2, 2019. Association for Computational Linguistics 2019, ISBN 978-1-950737-46-8 - Kai He, Jialun Wu, Xiaoyong Ma, Chong Zhang, Ming Huang, Chen Li, Lixia Yao:
Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research. 1-10 - Anne Dirkson
, Suzan Verberne, Wessel Kraaij:
Lexical Normalization of User-Generated Medical Text. 11-20 - Davy Weissenbacher, Abeed Sarker, Arjun Magge, Ashlynn R. Daughton, Karen O'Connor, Michael J. Paul, Graciela Gonzalez-Hernandez:
Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019. 21-30 - Maksim Belousov, William G. Dixon, Goran Nenadic:
MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation. 31-39 - Fionn Delahunty, Robert Johansson, Mihael Arcan:
Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety. 40-46 - Shuai Chen, Yuanhang Huang, Xiaowei Huang, Haoming Qin, Jun Yan, Buzhou Tang:
HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets. 47-51 - Zulfat Miftahutdinov, Ilseyar Alimova, Elena Tutubalina:
KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue. 52-57 - Tilia Ellendorff, Lenz Furrer, Nicola Colic, Noëmi Aepli, Fabio Rinaldi:
Approaching SMM4H with Merged Models and Multi-task Learning. 58-61 - Xinyan Zhao, Deahan Yu, V. G. Vinod Vydiswaran:
Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation. 62-70 - Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, Martin Jaggi:
Correlating Twitter Language with Community-Level Health Outcomes. 71-78 - Giuliano Tortoreto, Evgeny A. Stepanov, Alessandra Cervone, Mateusz Dubiel, Giuseppe Riccardi:
Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups More Therapeutic than Twitter? 79-88 - Anne Dirkson, Suzan Verberne:
Transfer Learning for Health-related Twitter Data. 89-92 - Javier Cortes-Tejada, Juan Martínez-Romo, Lourdes Araujo:
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets. 93-95 - Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang:
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention. 96-98 - Paul Barry, Özlem Uzuner:
Deep Learning for Identification of Adverse Effect Mentions In Twitter Data. 99-101 - Pilar López-Úbeda, Manuel Carlos Díaz-Galiano, María Teresa Martín-Valdivia, Luis Alfonso Ureña López:
Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media. 102-106 - V. G. Vinod Vydiswaran, Grace Ganzel, Bryan Romas, Deahan Yu, Amy Austin, Neha Bhomia, Socheatha Chan, Stephanie Hall, Van Le, Aaron Miller, Olawunmi Oduyebo, Aulia Song, Radhika Sondhi, Danny Teng, Hao Tseng, Kim Vuong, Stephanie Zimmerman:
Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1. 107-109 - Shubham Gondane:
Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings. 110-113 - Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Selene Baez Santamaría, Robert-Jan Sips:
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts. 114-116 - Chen-Kai Wang, Hong-Jie Dai, Bo-Hung Wang:
BIGODM System in the Social Media Mining for Health Applications Shared Task 2019. 117-119 - Sarah Sarabadani:
Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo. 120-122 - Parsa Bagherzadeh, Nadia Sheikh, Sabine Bergler:
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4. 123-126 - Debanjan Mahata, Sarthak Anand, Haimin Zhang, Simra Shahid, Laiba Mehnaz, Yaman Kumar, Rajiv Ratn Shah:
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter. 127-132 - Segun Taofeek Aroyehun, Alexander F. Gelbukh:
Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings. 133-135 - Samarth Rawal, Siddharth Rawal, Saadat Anwar, Chitta Baral:
Identification of Adverse Drug Reaction Mentions in Tweets - SMM4H Shared Task 2019. 136-137
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