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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.