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• W2953507155 abstract "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." @default.
• W2953507155 created "2019-07-12" @default.
• W2953507155 creator A5044509568 @default.
• W2953507155 date "2018-01-01" @default.
• W2953507155 modified "2023-09-26" @default.
• W2953507155 title "Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention" @default.
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• W2953507155 doi "https://doi.org/10.18653/v1/w18-5910" @default.
• W2953507155 hasPublicationYear "2018" @default.
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