{"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-unsupervised-word-sense-disambiguation","title":"An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages","arxiv_id":"1804.10686","date":"2018-04-27","proceeding":"LREC 2018 5","authors":["Dmitry Ustalov","Denis Teslenko","Alexander Panchenko","Mikhail Chernoskutov","Chris Biemann","Simone Paolo Ponzetto"],"abstract":"In this paper, we present Watasense, an unsupervised system for word sense\ndisambiguation. Given a sentence, the system chooses the most relevant sense of\neach input word with respect to the semantic similarity between the given\nsentence and the synset constituting the sense of the target word. Watasense\nhas two modes of operation. The sparse mode uses the traditional vector space\nmodel to estimate the most similar word sense corresponding to its context. The\ndense mode, instead, uses synset embeddings to cope with the sparsity problem.\nWe describe the architecture of the present system and also conduct its\nevaluation on three different lexical semantic resources for Russian. We found\nthat the dense mode substantially outperforms the sparse one on all datasets\naccording to the adjusted Rand index.","url_abs":"http://arxiv.org/abs/1804.10686v1","url_pdf":"http://arxiv.org/pdf/1804.10686v1.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-unsupervised-word-sense-disambiguation","repo_url":"https://github.com/nlpub/watasense","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"word-sense-disambiguation","task_name":"Word Sense Disambiguation"}],"methods":[],"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}