Abstract

This paper describes the systems developed for 1st and 2nd tasks of the 3rd Social Media Mining for Health Applications Shared Task at EMNLP 2018. The first task focuses on automatic detection of posts mentioning a drug name or dietary supplement, a binary classification. The second task is about distinguishing the tweets that present personal medication intake, possible medication intake and non-intake. We performed extensive experiments with various classifiers like Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDT) and deep learning architectures such as Long Short-Term Memory Networks (LSTM), jointed Convolutional Neural Networks (CNN) and LSTM architecture, and attention based LSTM architecture both at word and character level. We have also explored using various pre-trained embeddings like Global Vectors for Word Representation (GloVe), Word2Vec and task-specific embeddings learned using CNN-LSTM and LSTMs.

Highlights

  • The tasks (Davy Weissenbacher, 2018) involve NLP challenges on social media mining for health monitoring and surveillance and in particular pharmaco-vigilance

  • This paper proposes to explore how the emerging advantages of deep learning can be expanded upon to address the pertinent challenges for social media text analysis

  • The deep learning algorithm we put forward to use for these tasks differs from the existing methods in that our model takes advantage of the encoded local features extracted from the Convolutional Neural Networks (CNN) model and the longterm dependencies captured by the Long ShortTerm Memory Networks (LSTM) model

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Summary

Introduction

The tasks (Davy Weissenbacher, 2018) involve NLP challenges on social media mining for health monitoring and surveillance and in particular pharmaco-vigilance. This requires processing noisy, real-world, and substantially creative language expressions from social media. Deep learning has the potential to improve analysis of social media text because of its ability to learn patterns from unlabelled data (Arel et al, 2010). This property has enabled deep learning to produce breakthroughs in the domain of image, text and speech recognition.

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