Generating context templates for word sense disambiguation
Refereed conference paper presented and published in conference proceedings

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AbstractThis paper presents a novel approach for generating context templates for the task of word sense disambiguation (WSD). Context information of an ambiguous word, in form of feature vectors, is first classified into coarse-grained semantic categories by topic features using the latent dirichlet allocation (LDA) algorithm. To further refine the sense tags, all feature vectors of the ambiguous word, under the same topic, are recast into a network. Various centrality measures are derived to figure out the features or context words in the context templates, which are highly influential in the disambiguation. The WSD is achieved by identifying the maximum pairwise similarities between the context encoded in the templates and the sentence. The correct sense of an ambiguous word is resolved by distinguishing the most activated template without being trapped in a subjective linguistic quagmire. The approach is assessed in a corpus of more than 1,000,000 words. Experimental result shows the best measures perform comparably to the state-of-the-art. © Springer International Publishing 2013.
All Author(s) ListChan S.W.K.
Name of Conference26th Australasian Joint Conference on Artificial Intelligence, AI 2013
Start Date of Conference01/12/2013
End Date of Conference06/12/2013
Place of ConferenceDunedin
Country/Region of ConferenceNew Zealand
Detailed descriptionTo ORKTS: Also appeared in Lecture Notes in Computer Science, v. 8272, 466-477, Springer-Verlag
Volume Number8272 LNAI
PublisherSpringer Verlag
Place of PublicationGermany
Pages466 - 477
LanguagesEnglish-United Kingdom
KeywordsLatent dirichlet allocation, Network-based approach, Sense tagging

Last updated on 2020-10-07 at 02:58