Unsupervised induction of stochastic context-free grammars using distributional clustering

Alexander Clark
2001 Proceedings of the 2001 workshop on Computational Natural Language Learning - ConLL '01   unpublished
An algorithm is presented for learning a phrase-structure grammar from tagged text. It clusters sequences of tags together based on local distributional information, and selects clusters that satisfy a novel mutual information criterion. This criterion is shown to be related to the entropy of a random variable associated with the tree structures, and it is demonstrated that it selects linguistically plausible constituents. This is incorporated in a Minimum Description Length algorithm. The
more » ... algorithm. The evaluation of unsupervised models is discussed, and results are presented when the algorithm has been trained on 12 million words of the British National Corpus.
doi:10.3115/1117822.1117831 fatcat:pvwiroccwfezdhth4kj5g2pk4a