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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15898
Title: Unsupervised WSD by finding the predominant sense using context as a dynamic thesaurus
Authors: Tejada Cálrcamo, Javier
Calvo, Hiram
Gelbukh, Alex
Hara, Kazuo
Keywords: Distributional similarities;Maximization algorithm;Semantic similarity;Text corpora;Unsupervised method;Word sense;Word Sense Disambiguation;Word spaces;Semantics;Software agents;Thesauri;Natural language processing systems
Issue Date: 2010
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650204798&doi=10.1007%2fs11390-010-9385-2&partnerID=40&md5=99192aac46b7a3081deac681de4bf27b
Abstract: We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model. © 2010 Springer Science+Business Media, LLC & Science Press, China.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15898
ISSN: 10009000
Appears in Collections:Artículos de investigación

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