Margin perceptron for word sense disambiguation

Abstract

Word Sense Disambiguation (WSD) is an AI-complete problem where senses of words in the documents must be correctly selected from a senses inventory. Support Vector Machines (SVM) method has been successfully applied to supervised WSD. In contrast, perceptron has not been popular in supervised WSD. In this paper, a supervised method combining Margin Perceptron (MP) and Platt's probabilistic output is proposed to solve the word sense ambiguity problem. Experiments were conducted on Senseval-3 English Lexical Sample Task data set. The performance is comparable with systems using SVMs. Our system is in line with the best system participating in Senseval-3, regarding that we only used given training data, and no classifiers combination technique was applied. The advantage of our method is mainly two-fold: Firstly, good achieved performance shows that MP can be applied to problem with limited training data, especially in natural language processing. Secondly, MP algorithm used in this work is easy to implement, which benefits the application and the extension of the algorithm.