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A SVM Cascade for Agreement/Disagreement Classification

Pierre Andrews*, Suresh Manandhar**

* andrews@disi.unitn.it ; ** suresh@cs.york.ac.uk

* Dipartimento di Ingegneria e Scienza dell’Informazione
Università degli Studi di Trento
38050 Trento, Italy

** Department of Computer Science
The University of York
YO105DD York, United Kingdom


This article describes a method for classifying dialogue utterances and detecting the interlocutor’s agreement or disagreement. This labelling can help improve dialogue management by providing additional information on the utterance’s content without deep parsing. The proposed technique improves upon state of the art approaches by using a Support Vector Machine cascade. A combination of three binary support vector machines in a cascade is employed to filter out utterances that are easy to classify, thus reducing the noise in the learning of labels for more ambiguous utterances. The approach achieves higher accuracy (by 2.47%) than the state of the art while using a simpler approach which relies only on shallow local features of the utterances.


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Pierre Andrews, Suresh Manandhar
187 ko

TAL Volume 50 2009 . 3. Apprentissage automatique pour le TAL

Date de dernière mise à jour : 29 septembre 2010, auteur : Rédacteurs en chef.