A context expansion method for supervised word sense disambiguation

Francisco Tacoa; Danushka Bollegala; Mitsuru Ishizuka

doi:10.1109/ICSC.2012.27

A context expansion method for supervised word sense disambiguation

Francisco Tacoa, Danushka Bollegala, Mitsuru Ishizuka

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.

Original language	English
Title of host publication	Proceedings - IEEE 6th International Conference on Semantic Computing, ICSC 2012
Pages	339-341
Number of pages	3
DOIs	https://doi.org/10.1109/ICSC.2012.27
Publication status	Published - 2012
Externally published	Yes
Event	6th IEEE International Conference on Semantic Computing, ICSC 2012 - Palermo Duration: 2012 Sep 19 → 2012 Sep 21

Other

Other	6th IEEE International Conference on Semantic Computing, ICSC 2012
City	Palermo
Period	12/9/19 → 12/9/21

ASJC Scopus subject areas

Software

Access to Document

10.1109/ICSC.2012.27

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title = "A context expansion method for supervised word sense disambiguation",

abstract = "Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.",

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note = "6th IEEE International Conference on Semantic Computing, ICSC 2012 ; Conference date: 19-09-2012 Through 21-09-2012",

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AU - Tacoa, Francisco

AU - Bollegala, Danushka

AU - Ishizuka, Mitsuru

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AB - Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.

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