摘要
This paper presents an ontology-based partial pattern tree to identify the speech act in a spoken dialogue system. This study first extracts the key words/concepts in an application domain using latent semantic analysis (LSA). A partial pattern tree is used to deal with the ill-formed sentence problem in a spoken dialogue system. Concept expansion based on domain ontology is adopted to improve system performance. For performance evaluation, a medical dialogue system with multiple services, including registration information, clinic information and FAQ information, is implemented. Four performance measures were separately used for evaluation. The speech act identification rate achieves 86.2%. A Task Success Rate of 77% is obtained. The contextual appropriateness of the system response is 78.5%. Finally, the correct rate for FAQ retrieval is 82% with an improvement of 15% in comparison with the keyword-based vector space model. The results show the proposed ontology-based partial pattern tree is effective for dialogue management.
| 原文 | English |
|---|---|
| 頁面 | 2157-2160 |
| 頁數 | 4 |
| 出版狀態 | Published - 2004 一月 1 |
| 事件 | 8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of 持續時間: 2004 十月 4 → 2004 十月 8 |
Other
| Other | 8th International Conference on Spoken Language Processing, ICSLP 2004 |
|---|---|
| 國家 | Korea, Republic of |
| 城市 | Jeju, Jeju Island |
| 期間 | 04-10-04 → 04-10-08 |
指紋
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Linguistics and Language
引用此文
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Speech act identification using an ontology-based partial pattern tree. / Wu, Chung Hsien; Yeh, Jui Feng; Chen, Ming Jun.
2004. 2157-2160 論文發表於 8th International Conference on Spoken Language Processing, ICSLP 2004, Jeju, Jeju Island, Korea, Republic of.研究成果: Paper
TY - CONF
T1 - Speech act identification using an ontology-based partial pattern tree
AU - Wu, Chung Hsien
AU - Yeh, Jui Feng
AU - Chen, Ming Jun
PY - 2004/1/1
Y1 - 2004/1/1
N2 - This paper presents an ontology-based partial pattern tree to identify the speech act in a spoken dialogue system. This study first extracts the key words/concepts in an application domain using latent semantic analysis (LSA). A partial pattern tree is used to deal with the ill-formed sentence problem in a spoken dialogue system. Concept expansion based on domain ontology is adopted to improve system performance. For performance evaluation, a medical dialogue system with multiple services, including registration information, clinic information and FAQ information, is implemented. Four performance measures were separately used for evaluation. The speech act identification rate achieves 86.2%. A Task Success Rate of 77% is obtained. The contextual appropriateness of the system response is 78.5%. Finally, the correct rate for FAQ retrieval is 82% with an improvement of 15% in comparison with the keyword-based vector space model. The results show the proposed ontology-based partial pattern tree is effective for dialogue management.
AB - This paper presents an ontology-based partial pattern tree to identify the speech act in a spoken dialogue system. This study first extracts the key words/concepts in an application domain using latent semantic analysis (LSA). A partial pattern tree is used to deal with the ill-formed sentence problem in a spoken dialogue system. Concept expansion based on domain ontology is adopted to improve system performance. For performance evaluation, a medical dialogue system with multiple services, including registration information, clinic information and FAQ information, is implemented. Four performance measures were separately used for evaluation. The speech act identification rate achieves 86.2%. A Task Success Rate of 77% is obtained. The contextual appropriateness of the system response is 78.5%. Finally, the correct rate for FAQ retrieval is 82% with an improvement of 15% in comparison with the keyword-based vector space model. The results show the proposed ontology-based partial pattern tree is effective for dialogue management.
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M3 - Paper
AN - SCOPUS:85009083823
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EP - 2160
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