High WSD accuracy using Naive Bayesian classifier with rich features
Cuong Anh Le, Akira Shimazu · 2008
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.