Improving accuracy of intention-based response classification using decision tree.

This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show...

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Main Authors: Ali, S. A., Sulaiman, Md. Nasir, Mustapha, Aida, Mustapha, Norwati
Format: Article
Language:English
English
Published: Asian Network for Scientific Information 2009
Online Access:http://psasir.upm.edu.my/id/eprint/15143/
http://psasir.upm.edu.my/id/eprint/15143/1/Improving%20accuracy%20of%20intention.pdf
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author Ali, S. A.
Sulaiman, Md. Nasir
Mustapha, Aida
Mustapha, Norwati
author_facet Ali, S. A.
Sulaiman, Md. Nasir
Mustapha, Aida
Mustapha, Norwati
author_sort Ali, S. A.
building UPM Institutional Repository
collection Online Access
description This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show that decision tree able to achieve 81.95% recognition accuracy in classification better than the 73.9% obtained using Bayesian networks and 71.3% achieved by using Maximum likelihood estimation. This result showed that the performance of decision tree as classifier is well suited for these tasks.
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institution Universiti Putra Malaysia
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language English
English
last_indexed 2025-11-15T08:01:37Z
publishDate 2009
publisher Asian Network for Scientific Information
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spelling upm-151432015-11-24T03:46:13Z http://psasir.upm.edu.my/id/eprint/15143/ Improving accuracy of intention-based response classification using decision tree. Ali, S. A. Sulaiman, Md. Nasir Mustapha, Aida Mustapha, Norwati This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show that decision tree able to achieve 81.95% recognition accuracy in classification better than the 73.9% obtained using Bayesian networks and 71.3% achieved by using Maximum likelihood estimation. This result showed that the performance of decision tree as classifier is well suited for these tasks. Asian Network for Scientific Information 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15143/1/Improving%20accuracy%20of%20intention.pdf Ali, S. A. and Sulaiman, Md. Nasir and Mustapha, Aida and Mustapha, Norwati (2009) Improving accuracy of intention-based response classification using decision tree. Information Technology Journal, 8 (6). pp. 923-928. ISSN 1812-5638 10.3923/itj.2009.923.928 English
spellingShingle Ali, S. A.
Sulaiman, Md. Nasir
Mustapha, Aida
Mustapha, Norwati
Improving accuracy of intention-based response classification using decision tree.
title Improving accuracy of intention-based response classification using decision tree.
title_full Improving accuracy of intention-based response classification using decision tree.
title_fullStr Improving accuracy of intention-based response classification using decision tree.
title_full_unstemmed Improving accuracy of intention-based response classification using decision tree.
title_short Improving accuracy of intention-based response classification using decision tree.
title_sort improving accuracy of intention-based response classification using decision tree.
url http://psasir.upm.edu.my/id/eprint/15143/
http://psasir.upm.edu.my/id/eprint/15143/
http://psasir.upm.edu.my/id/eprint/15143/1/Improving%20accuracy%20of%20intention.pdf