{"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/one-single-deep-bidirectional-lstm-network","title":"One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data","arxiv_id":"1802.09059","date":"2018-02-25","proceeding":null,"authors":["Ahmad Pesaranghader","Ali Pesaranghader","Stan Matwin","Marina Sokolova"],"abstract":"Due to recent technical and scientific advances, we have a wealth of\ninformation hidden in unstructured text data such as offline/online narratives,\nresearch articles, and clinical reports. To mine these data properly,\nattributable to their innate ambiguity, a Word Sense Disambiguation (WSD)\nalgorithm can avoid numbers of difficulties in Natural Language Processing\n(NLP) pipeline. However, considering a large number of ambiguous words in one\nlanguage or technical domain, we may encounter limiting constraints for proper\ndeployment of existing WSD models. This paper attempts to address the problem\nof one-classifier-per-one-word WSD algorithms by proposing a single\nBidirectional Long Short-Term Memory (BLSTM) network which by considering\nsenses and context sequences works on all ambiguous words collectively.\nEvaluated on SensEval-3 benchmark, we show the result of our model is\ncomparable with top-performing WSD algorithms. We also discuss how applying\nadditional modifications alleviates the model fault and the need for more\ntraining data.","url_abs":"http://arxiv.org/abs/1802.09059v1","url_pdf":"http://arxiv.org/pdf/1802.09059v1.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":[],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"word-sense-disambiguation","task_name":"Word Sense Disambiguation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/word-sense-disambiguation-on-senseval-3","task":"Word Sense Disambiguation","dataset":"SensEval 3 Lexical Sample","model":"Single BiLSTM","rank_in_archive_order":4,"of":4,"metrics":{"F1":"72.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}