Worst-case complexity and empirical evaluation of artificial intelligence methods for unsupervised word sense disambiguation Online publication date: Mon, 31-Mar-2014
by Didier Schwab; Jérôme Goulian; Andon Tchechmedjiev
International Journal of Web Engineering and Technology (IJWET), Vol. 8, No. 2, 2013
Abstract: Word sense disambiguation (WSD) is a difficult problem for natural language processing. Algorithms that aim to solve the problem focus on the quality of the disambiguation alone and require considerable computational time. In this article, we focus on the study of three unsupervised stochastic algorithms for WSD: a genetic algorithm (GA) and a simulated annealing algorithm (SA) from the state of the art and our own ant colony algorithm (ACA). The comparison is made both in terms of the worst case computational complexity and of the empirical performance of the algorithms in terms of F1 scores, execution time and evaluation of the semantic relatedness measure. We find that ACA leads to a shorter execution time as well as better results that surpass the first sense baseline and come close to the results of supervised systems on the coarse-grained all words task from Semeval 2007.
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