Dialogue act recognition under uncertainty using bayesian networks

Authors:
Simon Keizer;Rieks op den Akker
Affiliations:
Computational linguistics & artificial intelligence group, faculty of arts, tilburg university, p.o. box 90153, 5000 le tilburg, the netherlands email: s.keizer@uvt.nl;Human media interaction group, department of electrical engineering, mathematics and computer science, university of twente, p.o. box 217, 7500 ae enschede, the netherlands email: infrieks@cs.utwe ...
Venue:
Natural Language Engineering
Year:
2007

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Abstract

In this paper we discuss the task of dialogue act recognition as a part of interpreting user utterances in context. To deal with the uncertainty that is inherent in natural language processing in general and dialogue act recognition in particular we use machine learning techniques to train classifiers from corpus data. These classifiers make use of both lexical features of the (Dutch) keyboard-typed utterances in the corpus used, and context features in the form of dialogue acts of previous utterances. In particular, we consider probabilistic models in the form of Bayesian networks to be proposed as a more general framework for dealing with uncertainty in the dialogue modelling process.