Papers
Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling
Farhana Ferdousi Liza, Marek Grzes
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing
Chunyang Xiao, Christoph Teichmann, Konstantine Arkoudas
Sequential Neural Networks as Automata
William Merrill
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges
Jason Eisner, Matthias Gallé, Jeffrey Heinz et al.
Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019
Samarth Rawal, Siddharth Rawal, Saadat Anwar et al.
Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings
Segun Taofeek Aroyehun, Alexander Gelbukh
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter
Debanjan Mahata, Sarthak Anand, Haimin Zhang et al.
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4
Parsa Bagherzadeh, Nadia Sheikh, Sabine Bergler
Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo
Sarah Sarabadani
BIGODM System in the Social Media Mining for Health Applications Shared Task 2019
Chen-Kai Wang, Hong-Jie Dai, Bo-Hung Wang
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts
Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon et al.
Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
V.G.Vinod Vydiswaran, Grace Ganzel, Bryan Romas et al.
Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media
Pilar López Úbeda, Manuel Carlos Díaz Galiano, Maite Martin et al.
Deep Learning for Identification of Adverse Effect Mentions In Twitter Data
Paul Barry, Ozlem Uzuner
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention
Suyu Ge, Tao Qi, Chuhan Wu et al.
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets
Javier Cortes-Tejada, Juan Martinez-Romo, Lourdes Araujo
Transfer Learning for Health-related Twitter Data
Anne Dirkson, Suzan Verberne
Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups More Therapeutic than Twitter?
Giuliano Tortoreto, Evgeny Stepanov, Alessandra Cervone et al.
Correlating Twitter Language with Community-Level Health Outcomes
Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean et al.
Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation
Xinyan Zhao, Deahan Yu, V.G.Vinod Vydiswaran
Approaching SMM4H with Merged Models and Multi-task Learning
Tilia Ellendorff, Lenz Furrer, Nicola Colic et al.
KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue
Zulfat Miftahutdinov, Ilseyar Alimova, Elena Tutubalina
HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets
Shuai Chen, Yuanhang Huang, Xiaowei Huang et al.
Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety
Fionn Delahunty, Robert Johansson, Mihael Arcan