Berg-Kirkpatrick et al, ACL 2010: Painless Unsupervised Learning with Features

Citation

T. Berg-Kirkpatrick, A. Bouchard-Côté, J. DeNero, and D. Klein. Painless Unsupervised Learning with Features, Human Language Technologies 2010, pp. 582-590, Los Angeles, June 2010.

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Summary

This paper generalizes conventional HMMs to featurized HMMs, by replacing the multinomial conditional probability distributions (CPDs) with miniature log-linear models. Two algorithms for unsupervised training of featurized HMMs are proposed.

Featurized HMMs are applied to four unsupervised learning tasks:

• POS induction (unsupervised version of POS tagging);
• Grammar induction;
• Word alignment;
• Word segmentation.

For all these four tasks, featurized HMMs are shown to outperform their unfeaturized counterparts by a substantial margin.