• 人工智能与模式识别 •

### 模糊概念图匹配的语用推理研究

1. 1. 西安建筑科技大学 信息与控制工程学院，西安 710055
2. 陕西彩虹电子玻璃有限公司，陕西 咸阳 712000
• 出版日期:2017-09-01 发布日期:2017-09-06

### Research on Pragmatic Inference of Fuzzy Conceptual Graph Matching

LIU Peiqi1, HUANG Miao1+, FENG Hao1, ZHOU Wei2

1. 1. School of Information and Control Engineering, Xi??an University of Architecture and Technology, Xi'an 710055, China
2. Shaanxi Caihong Electronic Glass Co., Ltd., Xianyang, Shaanxi 712000, China
• Online:2017-09-01 Published:2017-09-06

Abstract: Focused on the issue that computer cannot automatically carry out a pragmatic analysis of the deep meaning    of whole discourse at present, this paper designs the relevance inference algorithm based on fuzzy conceptual graph. In the algorithm, aiming at the specific dialog mode of Chinese pragmatic analysis, the discourses of speakers and the knowledge of cognitive context are expressed in fuzzy conceptual graph, and the relevance inference is conducted from computer science. The problem that computer automatically deduces the deep meaning of whole discourse is resolved. Through the experimental analysis, accuracy can reach 78%. In addition, the algorithm has been applied in analyzing public opinion and mining social network. After the preprocessed discourses of speaker by this relevance inference algorithm based on fuzzy conceptual graph, this algorithm can reduce the missed alarm rate and false alarm rate of Mutton and AdaBoost methods based on multi-features fusion and increase the accuracy of SBV polar transfer algorithm. The algorithm can deduce the deeper meaning of answerer??s discourse at specific dialog mode.