A machine learning approach to speech act classification using function words

Authors:
James O'Shea;Zuhair Bandar;Keeley Crockett
Affiliations:
Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom
Venue:
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Year:
2010

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Abstract

This paper presents a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. It focuses on classifying questions or non-questions as a generally useful task in agent-based systems. The proposed technique extracts salient features by replacing function words with numeric tokens and replacing each content word with a standard numeric wildcard token. The Decision Tree, which is a well-established classification technique, has been chosen for this work. Experiments provide evidence of potential for highly effective classification, with a significant achievement on a challenging dataset, before any optimisation of feature extraction has taken place.