Abstract
We address the task of unsupervised POS tagging. We demonstrate that good results can be obtained using the robust EM-HMM learner when provided with good initial conditions, even with incomplete dictionaries. We present a family of algorithms to compute effective initial estimations p(t|w). We test the method on the task of full morphological disambiguation in Hebrew achieving an error reduction of 25% over a strong uniform distribution baseline. We also test the same method on the standard WSJ unsupervised POS tagging task and obtain results competitive with recent state-ofthe- art methods, while using simple and efficient learning methods.
| Original language | English |
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| Title of host publication | ACL-08 |
| Subtitle of host publication | HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference |
| Pages | 746-754 |
| Number of pages | 9 |
| State | Published - 2008 |
| Externally published | Yes |
| Event | 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT - Columbus, OH, United States Duration: 15 Jun 2008 → 20 Jun 2008 |
Publication series
| Name | ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference |
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Conference
| Conference | 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT |
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| Country/Territory | United States |
| City | Columbus, OH |
| Period | 15/06/08 → 20/06/08 |
Bibliographical note
Funding Information:Funding. Funding has been provided in the form of a grant for clinical cancer research from the Ministry of Health, Labour and Welfare, Japan.