Large Margin Methods for Part of Speech Tagging
Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
Part of speech tagging, an important component of speech recognition
systems, is a sequence labeling problem which involves
inferring a state sequence from an observation sequence,
where the state sequence encodes a labeling, annotation or
segmentation of an observation sequence. In this paper we give an overview
of discriminative methods developed for this problem. Special
emphasis is put on
large margin methods by generalizing multiclass Support Vector
Machines and AdaBoost to the case of label sequences.
Experimental evaluation on Part of Speech Tagging
demonstrates the advantages of these models over classical approaches
like Hidden Markov Models and their competitiveness
with methods like Conditional Random Fields.