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Large Margin Methods for Part‐of‐Speech Tagging

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Altun,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Altun, Y. (2009). Large Margin Methods for Part‐of‐Speech Tagging. In J. Keshet, & S. Bengio (Eds.), Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods (pp. 139-158). Hoboken, NJ, USA: Wiley.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C5F9-2
Abstract
Part of speech tagging, an important component of speech recognition systems, is a sequence labeling problem which involves in- ferring a state sequence from an observation sequence, where the state sequence encodes a labeling, annotation or segmentation of an observa- tion sequence. In this paper we give an overview of discriminative meth- ods developed for this problem. Special emphasis is put on large margin methods by generalizing multiclass Support Vector Machines and Ad- aBoost to the case of label sequences. Experimental evaluation on Part of Speech Tagging demonstrates the advantages of these models over clas- sical approaches like Hidden Markov Models and their competitiveness with methods like Conditional Random Fields.