{"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/knowledge-based-word-sense-disambiguation","title":"Knowledge-based Word Sense Disambiguation using Topic Models","arxiv_id":"1801.01900","date":"2018-01-05","proceeding":null,"authors":["Devendra Singh Chaplot","Ruslan Salakhutdinov"],"abstract":"Word Sense Disambiguation is an open problem in Natural Language Processing\nwhich is particularly challenging and useful in the unsupervised setting where\nall the words in any given text need to be disambiguated without using any\nlabeled data. Typically WSD systems use the sentence or a small window of words\naround the target word as the context for disambiguation because their\ncomputational complexity scales exponentially with the size of the context. In\nthis paper, we leverage the formalism of topic model to design a WSD system\nthat scales linearly with the number of words in the context. As a result, our\nsystem is able to utilize the whole document as the context for a word to be\ndisambiguated. The proposed method is a variant of Latent Dirichlet Allocation\nin which the topic proportions for a document are replaced by synset\nproportions. We further utilize the information in the WordNet by assigning a\nnon-uniform prior to synset distribution over words and a logistic-normal prior\nfor document distribution over synsets. We evaluate the proposed method on\nSenseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English\nAll-Word WSD datasets and show that it outperforms the state-of-the-art\nunsupervised knowledge-based WSD system by a significant margin.","url_abs":"http://arxiv.org/abs/1801.01900v1","url_pdf":"http://arxiv.org/pdf/1801.01900v1.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":"sentence","task_name":"Sentence"},{"task_slug":"topic-models","task_name":"Topic Models"},{"task_slug":"word-sense-disambiguation","task_name":"Word Sense Disambiguation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/word-sense-disambiguation-on-knowledge-based","task":"Word Sense Disambiguation","dataset":"Knowledge-based:","model":"WSD-TM","rank_in_archive_order":2,"of":6,"metrics":{"All":"66.9","SemEval 2007":"**55.6**","SemEval 2013":"65.3","SemEval 2015":"69.6","Senseval 2":"**69.0**","Senseval 3":"**66.9**"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.01900","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}