NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets

2019-08-01 · WS 2019 8 · Javier Cortes-Tejada, Juan Martinez-Romo, Lourdes Araujo

This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.

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Tasks

Word Embeddings

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

GloVe GloVe Embeddings are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares…

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Approaching SMM4H with Merged Models and Multi-task Learning

2019-08-01 · WS 2019 8 · Tilia Ellendorff, Lenz Furrer, Nicola Colic, No{\"e}mi Aepli 외

We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions i…

Multi-Task Learning

Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines

2020-12-01 · SMM4H (COLING) 2020 12 · Sougata Saha, Souvik Das, Prashi Khurana, Rohini Srihari

This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020. We specifically describe the systems designed to solve task 2: Automatic classification of multilingual tweet…

NER Question Answering Task 2

Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets

2021-06-01 · NAACL (SMM4H) 2021 6 · George-Andrei Dima, Dumitru-Clementin Cercel, Mihai Dascalu

This paper presents our contribution to the Social Media Mining for Health Applications Shared Task 2021. We addressed all the three subtasks of Task 1: Subtask A (classification of tweets containing adverse effects), Su…

Multi-Task Learning

The side effect profile of Clozapine in real world data of three large mental hospitals

2020-01-27 · Ehtesham Iqbal, Risha Govind, Alvin Romero, Olubanke Dzahini 외

Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control tr…

Management

LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets

2020-12-01 · SMM4H (COLING) 2020 12 · Ludovic Tanguy, Lydia-Mai Ho-Dac, Cécile Fabre, Roxane Bois 외

This paper describes our participation to the SMM4H shared task 2. We designed a rule-based classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English an…

regression Task 2