{"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/an-investigation-of-recurrent-neural","title":"An Investigation of Recurrent Neural Architectures for Drug Name Recognition","arxiv_id":"1609.07585","date":"2016-09-24","proceeding":"WS 2016 11","authors":["Raghavendra Chalapathy","Ehsan Zare Borzeshi","Massimo Piccardi"],"abstract":"Drug name recognition (DNR) is an essential step in the Pharmacovigilance\n(PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical\ntexts and classify them into predefined categories. State-of-the-art DNR\napproaches heavily rely on hand crafted features and domain specific resources\nwhich are difficult to collect and tune. For this reason, this paper\ninvestigates the effectiveness of contemporary recurrent neural architectures -\nthe Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at\nperforming DNR straight from the text. The experimental results achieved on the\nauthoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional\nLSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.","url_abs":"http://arxiv.org/abs/1609.07585v1","url_pdf":"http://arxiv.org/pdf/1609.07585v1.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":"an-investigation-of-recurrent-neural","repo_url":"https://github.com/raghavchalapathy/dnr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"pharmacovigilance","task_name":"Pharmacovigilance"}],"methods":[{"method_slug":"crf","method_name":"CRF"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}