Title :
A Context Expansion Method for Supervised Word Sense Disambiguation
Author :
Tacoa, Francisco ; Bollegala, Danushka ; Ishizuka, Mitsuru
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Univ. of Tokyo, Tokyo, Japan
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.
Keywords :
natural language processing; thesauri; WSD systems; candidate selection; context expansion method; feature sparseness; mutual relatedness; supervised word sense disambiguation; thesaurus; Additives; Context; Equations; Mathematical model; Thesauri; Training; Vectors;
Conference_Titel :
Semantic Computing (ICSC), 2012 IEEE Sixth International Conference on
Conference_Location :
Palermo
Print_ISBN :
978-1-4673-4433-3
DOI :
10.1109/ICSC.2012.27
