A Maximum Entropy-based Word Sense Disambiguation System

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A Maximum Entropy-based Word Sense Disambiguation System
In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed using the SENSEVAL-2 data for the Spanish lexical sample task. Such analysis shows that instead of training with the same kind of information for all words, each one is more effectively learned using a different set of features. This bestfeature-selection is used to build some systems based on different maximum entropy classifiers, and a voting system helped by a knowledgebased method.
Armando Suárez, Manuel Palomar
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2002
Authors Armando Suárez, Manuel Palomar
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