This paper presents an unsupervised word sense learning algorithm, which induces senses of target word by grouping its occurrences into a “natural” number of clusters based on the similarity of their contexts. For removing noisy words in feature set, feature selection is conducted by optimizing a cluster validation criterion subject to some constraint in an unsupervised manner. Gaussian mixture model and Minimum Description Length criterion are used to estimate cluster structure and cluster number. .
