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Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention

1 Oct 2018WS 2018 10archive 2025-07-28

Orest Xherija

This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.

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General Classification Text Classification Word Embeddings

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Adam BiLSTM Dropout Embedding Dropout Gradient Clipping LSTM Recurrent Dropout Sigmoid Activation Tanh Activation

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