Boosting Applied Toe 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.
Cite
Text
Escudero et al. "Boosting Applied Toe Word Sense Disambiguation." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_14Markdown
[Escudero et al. "Boosting Applied Toe Word Sense Disambiguation." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/escudero2000ecml-boosting/) doi:10.1007/3-540-45164-1_14BibTeX
@inproceedings{escudero2000ecml-boosting,
title = {{Boosting Applied Toe Word Sense Disambiguation}},
author = {Escudero, Gerard and Màrquez, Lluís and Rigau, German},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {129-141},
doi = {10.1007/3-540-45164-1_14},
url = {https://mlanthology.org/ecmlpkdd/2000/escudero2000ecml-boosting/}
}