Part of Speech Induction using Non-negative Matrix Factorization

Unsupervised part-of-speech induction involves the discovery of syntactic categories in a text, given no additional information other than the text itself. One requirement of an induction system is the ability to handle multiple categories for each word, in order to deal with word sense ambiguity. We construct an algorithm for unsupervised part-of-speech induction, treating the problem as one of soft clustering. The key technical component of the algorithm is the application of the recently developed technique of non-negative matrix factorization to the task of category discovery, using word contexts and morphology as syntactic cues.