Abstract
In this paper Schapire and Singer’s AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on supervised WSD. In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating the algorithm by reducing the feature space are studied. The best variant, which we call LazyBoosting, is tested on the largest sense-tagged corpus available containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmark algorithms.
This research has been partially funded by the Spanish Research Department (CICYT’s BASURDE project TIC98-0423-C06) and by the Catalan Research Department (CIRIT’s consolidated research group 1999SGR-150, CREL’s Catalan WordNet project and CIRIT’s grant 1999FI 00773).
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Escudero, G., Màrquez, L., Rigau, G. (2000). Boosting Applied to Word Sense Disambiguation. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_14
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