Understanding the Yarowsky Algorithm

Author:

Abney Steven1

Affiliation:

1. University of Michigan, 4080 Frieze Bldg., 105 S. State Street, Ann Arbor, MI 48109-1285.

Abstract

Many problems in computational linguistics are well suited for bootstrapping (semisupervised learning) techniques. The Yarowsky algorithm is a well-known bootstrapping algorithm, but it is not mathematically well understood. This article analyzes it as optimizing an objective function. More specifically, a number of variants of the Yarowsky algorithm (though not the original algorithm itself) are shown to optimize either likelihood or a closely related objective function K.

Publisher

MIT Press - Journals

Subject

Artificial Intelligence,Computer Science Applications,Linguistics and Language,Language and Linguistics

Reference5 articles.

1. Abney, Steven. 2002. Bootstrapping. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pages 360-367.

2. Blum, Avrim and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT), pages 92-100. Morgan Kaufmann, San Francisco.

3. Collins, Michael and Yoram Singer. 1999. Unsupervised models for named entity classification. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP), College Park, MD, pages 100-110.

4. Dasgupta, Sanjoy, Michael Littman, and David McAllester. 2001. PAC generalization bounds for co-training. In Proceedings of Advances in Neural Information Processing Systems 14 (NIPS), Vancouver, British Columbia, Canada.

5. Yarowsky, David. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, Cambridge, MA, pages 189-196.

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