Abstract
This paper aims to identify the communication goal(s) of a user’s information-seeking query out of a finite set of within-domain goals in natural language queries. It proposes using Tree-Augmented Naive Bayes networks (TANs) for goal detection. The problem is formulated as N binary decisions, and each is performed by a TAN. Comparative study has been carried out to compare the performance with Naive Bayes, fully-connected TANs, and multi-layer neural networks. Experimental results show that TANs consistently give better results when tested on the ATIS and DARPA Communicator corpora.
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He, Y. (2010). Goal Detection from Natural Language Queries. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds) Natural Language Processing and Information Systems. NLDB 2010. Lecture Notes in Computer Science, vol 6177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13881-2_16
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DOI: https://doi.org/10.1007/978-3-642-13881-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13880-5
Online ISBN: 978-3-642-13881-2
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