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|DOI (Digital Object Identifier) Link:||http://dx.doi.org/10.1007/978-3-642-13881-2_16|
|Google Scholar:||Look up in Google Scholar|
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.
|Item Type:||Conference Item|
|Copyright Holders:||2010 Springer-Verlag Berlin Heidelberg|
|Extra Information:||The original publication is available at www.springerlink.com|
|Keywords:||goal detection; tree-augmented naive Bayes networks (TANs); natural language query|
|Academic Unit/Department:||Knowledge Media Institute|
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||30 Sep 2010 11:31|
|Last Modified:||26 Oct 2012 04:31|
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