K-means clustering to improve the accuracy of decision tree response classification.
The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification ta...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Asian Network for Scientific Information (ANSINET)
2009
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| Online Access: | http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf |
| _version_ | 1796968839380992000 |
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| author | Ali, S. A. Sulaiman , N. Mustapha, Aida Mustapha, Norwati |
| author_facet | Ali, S. A. Sulaiman , N. Mustapha, Aida Mustapha, Norwati |
| author_sort | Ali, S. A. |
| collection | UPM |
| description | The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone. |
| first_indexed | 2024-03-06T07:34:12Z |
| format | Article |
| id | upm.eprints-15392 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T07:34:12Z |
| publishDate | 2009 |
| publisher | Asian Network for Scientific Information (ANSINET) |
| record_format | dspace |
| spelling | upm.eprints-153922015-11-24T04:25:19Z http://psasir.upm.edu.my/id/eprint/15392/ K-means clustering to improve the accuracy of decision tree response classification. Ali, S. A. Sulaiman , N. Mustapha, Aida Mustapha, Norwati The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone. Asian Network for Scientific Information (ANSINET) 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf Ali, S. A. and Sulaiman , N. and Mustapha, Aida and Mustapha, Norwati (2009) K-means clustering to improve the accuracy of decision tree response classification. Information Technology Journal, 8 (8). pp. 1256-1262. ISSN 1812-5638 10.3923/itj.2009.1256.1262 English |
| spellingShingle | Ali, S. A. Sulaiman , N. Mustapha, Aida Mustapha, Norwati K-means clustering to improve the accuracy of decision tree response classification. |
| title | K-means clustering to improve the accuracy of decision tree response classification. |
| title_full | K-means clustering to improve the accuracy of decision tree response classification. |
| title_fullStr | K-means clustering to improve the accuracy of decision tree response classification. |
| title_full_unstemmed | K-means clustering to improve the accuracy of decision tree response classification. |
| title_short | K-means clustering to improve the accuracy of decision tree response classification. |
| title_sort | k means clustering to improve the accuracy of decision tree response classification |
| url | http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf |
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