{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/classification-of-medication-related-tweets","title":"Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention","arxiv_id":null,"date":"2018-10-01","proceeding":"WS 2018 10","authors":["Orest Xherija"],"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.","url_abs":"https://aclanthology.org/W18-5910","url_pdf":"https://aclanthology.org/W18-5910.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"classification-of-medication-related-tweets","repo_url":"https://github.com/orestxherija/smm4h2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"embedding-dropout","method_name":"Embedding Dropout"},{"method_slug":"gradient-clipping","method_name":"Gradient Clipping"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"recurrent-dropout","method_name":"Recurrent Dropout"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}