Date of Original Version
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Copyright 2010 ACL
Abstract or Description
We describe a method of incorporating taskspecific cost functions into standard conditional log-likelihood (CLL) training of linear structured prediction models. Recently introduced in the speech recognition community, we describe the method generally for structured models, highlight connections to CLL and max-margin learning for structured prediction (Taskar et al., 2003), and show that the method optimizes a bound on risk. The approach is simple, efficient, and easy to implement, requiring very little change to an existing CLL implementation. We present experimental results comparing with several commonly-used methods for training structured predictors for named-entity recognition.
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Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 733-736.