Exemplar-Based Word Sense Disambiguation: Some Recent Improvements
Hwee Tou Ng (DSO National Laboratories)

TL;DR
This paper presents recent enhancements to exemplar-based word sense disambiguation, notably using larger k values and cross-validation, achieving accuracy comparable to the best existing methods on a large sense-tagged corpus.
Contribution
The paper introduces improvements to exemplar-based disambiguation by optimizing k and using cross-validation, resulting in higher accuracy on a large corpus.
Findings
Achieved higher disambiguation accuracy with larger k values.
Automatic k selection via cross-validation improves performance.
Comparable accuracy to the Naive-Bayes algorithm on the same dataset.
Abstract
In this paper, we report recent improvements to the exemplar-based learning approach for word sense disambiguation that have achieved higher disambiguation accuracy. By using a larger value of , the number of nearest neighbors to use for determining the class of a test example, and through 10-fold cross validation to automatically determine the best , we have obtained improved disambiguation accuracy on a large sense-tagged corpus first used in \cite{ng96}. The accuracy achieved by our improved exemplar-based classifier is comparable to the accuracy on the same data set obtained by the Naive-Bayes algorithm, which was reported in \cite{mooney96} to have the highest disambiguation accuracy among seven state-of-the-art machine learning algorithms.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Speech and dialogue systems
