Semantic Dependency Based Natural Language Understanding in a Medical Dialogue System

• Main Authors: Mao-Zhu Yang, 楊茂柱
• Other Authors: Chung-Hsien Wu
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/2avpug

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 92 === 　　In the high information-intensive society, one of the most ideal man-machine interactive communications is the dialogue system using natural language in the near future. The misunderstanding in the semantic interpreter usually result in the un-complete dialog in the traditional dialog management, especial in the speech act or intention identification. The understanding of the utterance of the user will become the most interesting research issue. This thesis mainly proposes a novel understanding approach called by Semantic Dependency Analysis (SDA), which purpose is to find the implicit semantic dependence between the concepts. Besides definitions of the semantic concepts, the dependence structures between concepts in the utterance are also took into consideration. Instead of semantic frame/slot, SDA can keep the more information when the system can not clearly identify speech act or intention. 　　This thesis also uses dialogue history to help understanding the utterances. The Semantic Dependency Relations are built according to the structure of sentence and the conceptual meaning of the words. When developing of the system，we use Sinica TreeBank and HowNet as the system knowledge. 　　In order to evaluation the method we proposed, the medical service dialog system is developed. The accuracy rate of speech act detection is 95.6%, the task-completion rate is 85.24%, and the average number of turns of each conversation is 8.3. Compared with the Bayesian Classifier and Partial-Pattern Tree based approaches, we have 14.9% and 12.47% improvement in accuracy rate of Speech Act respectively. The result showed that the performance of the proposed method is obviously improved, namely the SDA approach outperforms the traditional approaches in the semantic understanding of spoken dialog system.