Learning word senses with feature selection and order identification capabilities
Zheng-Yu Niu, Donghong Ji, Chew-Lim Tan · 2004
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. Experimental results show that our algorithm can find important feature subset, estimate model order (cluster number) and achieve better performance than another algorithm which requires cluster number to be provided.