High WSD Accuracy Using Naive Bayesian Classifier with Rich Features

It is shown that by adding more rich knowledge, represented by ordered words in a local context and collocations, the Naive Bayesian classifier can achieve higher accuracy in comparison with the best previously published results.

Abstract

Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Using Naive Bayesian (NB) classifiers is known as one of the best methods for supervised approaches for WSD (Mooney, 1996; Pedersen, 2000), and this model usually uses only a topic context represented by unordered words in a large context. In this paper, we show that by adding more rich knowledge, represented by ordered words in a local context and collocations, the NB classifier can achieve higher accuracy in comparison with the best previously published results. The features were chosen using a forward sequential selection algorithm. Our experiments obtained 92.3 % accuracy for four common test words (interest, line, hard, serve). We also tested on a large dataset, the DSO corpus, and obtained accuracies of 66.4 % for verbs and 72.7 % for nouns.