{"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/neural-sequence-learning-models-for-word","title":"Neural Sequence Learning Models for Word Sense Disambiguation","arxiv_id":null,"date":"2017-09-01","proceeding":"EMNLP 2017 9","authors":["Aless Raganato","ro","Claudio Delli Bovi","Roberto Navigli"],"abstract":"Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.","url_abs":"https://aclanthology.org/D17-1120","url_pdf":"https://aclanthology.org/D17-1120.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":"decoder","task_name":"Decoder"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"word-sense-disambiguation","task_name":"Word Sense Disambiguation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/word-sense-disambiguation-on-supervised","task":"Word Sense Disambiguation","dataset":"Supervised:","model":"Bi-LSTM<sub>att+LEX</sub>","rank_in_archive_order":22,"of":27,"metrics":{"SemEval 2007":"63.7*","SemEval 2013":"66.4","SemEval 2015":"72.4","Senseval 2":"72.0","Senseval 3":"69.4"},"uses_additional_data":false},{"leaderboard":"/sota/word-sense-disambiguation-on-supervised","task":"Word Sense Disambiguation","dataset":"Supervised:","model":"Bi-LSTM<sub>att+LEX+POS</sub>","rank_in_archive_order":23,"of":27,"metrics":{"SemEval 2007":"64.8*","SemEval 2013":"66.9","SemEval 2015":"71.5","Senseval 2":"72.0","Senseval 3":"69.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}