Boosting Applied to Word Sense Disambiguation

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.

12 pages

References in corpus (2)

• Selective Sampling for Example-based Word Sense Disambiguation
• Minimizing Manual Annotation Cost In Supervised Training From Corpora

Cited by in corpus (5)

• Machine Learning in Automated Text Categorization
• Word sense disambiguation: a survey
• Extensions of stability selection using subsamples of observations and covariates
• No Spare Parts: Sharing Part Detectors for Image Categorization
• A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation