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Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
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COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
SemEval-2007 task 07: coarse-grained English all-words task
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IEEE Transactions on Evolutionary Computation
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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.