SenseDependency-rank: A word sense disambiguation method based on random walks and dependency trees

Marco Antonio Sobrevilla-Cabezudo, Arturo Oncevay-Marcos, Andrés Melgar

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1 Cita (Scopus)

Resumen

Word Sense Disambiguation (WSD) is the field that seeks to determine the correct sense of a word in a given context. In this paper, we present a WSD method based on random walks over a dependency tree, whose nodes are word-senses from the WordNet. Besides, our method incorporates prior knowledge about the frequency of use of the word-senses. We observed that our results outperform several graph-based WSD methods in All-Word task of SensEval-2 and SensEval-3, including the baseline, where the nouns and verbs part-of-speech show the better improvement in their F-measure scores.

Idioma originalInglés
Título de la publicación alojadaComputational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers
EditoresAlexander Gelbukh
EditorialSpringer Verlag
Páginas185-194
Número de páginas10
ISBN (versión impresa)9783319771120
DOI
EstadoPublicada - 2018
Evento18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 - Budapest, Hungría
Duración: 17 abr. 201723 abr. 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10761 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017
País/TerritorioHungría
CiudadBudapest
Período17/04/1723/04/17

Huella

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