Improving Word Sense Disambiguation Accuracy Using Naive Bayesian Classifier with Rich Features

摘要

Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Resolving the ambiguity of words is one of the most important open problems in Natural Language Processing (NLP) field. It is applied to many NLP tasks such as machine translation, information retrieval, parsing, and text summarization. The Naive Bayesian (NB) classifier is known as one of the best methods for supervised approaches such as in [7, 9], 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, WSD using a 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 the accuracies of 66.2% for verbs and 73.2% for nouns.