Difference between revisions of "Berg-Kirkpatrick et al, ACL 2010: Painless Unsupervised Learning with Features"
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== Online Version == | == Online Version == | ||
| − | [http://www.aclweb.org/anthology-new/N/N10/N10-1083.pdf | + | [http://www.aclweb.org/anthology-new/N/N10/N10-1083.pdf PDF version] |
== Summary == | == 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. | + | This [[Category::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: | Featurized HMMs are applied to four unsupervised learning tasks: | ||
Revision as of 16:50, 17 September 2011
Contents
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.
Online Version
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.
