Large margin methods for label sequence learning

An overview of discriminative methods developed for label sequence learning by generalizing multiclass Support Vector Machines and AdaBoost to the case of label sequences and demonstrating the advantages over classical approaches and the competitiveness with methods like Conditional Random Fields.

Abstract

Label sequence learning is the problem of inferring a state sequence from an observation sequence, where the state sequence may encode a labeling, annotation or segmentation of the 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. An experimental evaluation demonstrates the advantages over classical approaches like Hidden Markov Models and the competitiveness with methods like Conditional Random Fields.