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...
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| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
Asian Network for Scientific Information (ANSINET)
2009
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf http://psasir.upm.edu.my/id/eprint/15392/ |
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| 要約: | 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. |
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