On Some Optimization Heuristics for Lesk-Like WSD Algorithms
For most English words, dictionaries give various senses: e.g., “bank”can stand for a financial institution, shore, set, etc. Automatic selection of the sense intended in a given text has crucial importance in many applications of text processing, such as information retrieval or machine translation: e.g., “(my account in the) bank” is to be translated into Spanish as “(mi cuenta en el) banco” whereas “(on the) bank (of the lake)” as “(en la) orilla (del lago).” To choose the optimal combination of the intended senses of all words, Lesk suggested to consider the global coherence of the text, i.e., which we mean the average relatedness between the chosen senses for all words in the text. Due to high dimensionality of the search space, heuristics are to be used to find a near-optimal configuration. In this paper, we discuss several such heuristics that differ in terms of complexity and quality of the results. In particular, we introduce a dimensionality reduction algorithm that reduces the complexity of computationally expensive approaches such as genetic algorithms.
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- 1.Cowie, J., Guthrie, J.A., Guthrie, L.: Lexical disambiguation using simulated annealing. In: Proc. of the International Conference on Computational Linguistics, pp. 359–365 (1992)Google Scholar
- 2.Edmonds, P., Kilgarriff, A. (eds.): Journal of Natural Language Engineering, 9(1) (2003), Special issue based on Senseval-2, www.senseval.org
- 3.Gale, W., Church, K., Yarowsky, D.: One sense per discourse. In: Proc. of the DARPA Speech and Natural Language workshop, Harriman, NY (February 1992)Google Scholar
- 4.Gelbukh, A., Sidorov, G., Han, S.-Y.: Evolutionary Approach to Natural Language Word Sense Disambiguation through Global Coherence Optimization. WSEAS Transactions on Communications 1(2), 11–19 (2003)Google Scholar
- 5.Lesk, M.: Automatic sense disambiguation using machine-readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proc. of ACM SIGDOC Conference, Toronto, Canada, pp. 24–26 (1986)Google Scholar