A Feature-Enriched Method for User Intent Classification by Leveraging Semantic Tag Expansion

摘要：

　　User intent identification and classification has become a vital topic of query understanding in human-computer dialogue applications.The identification of users intent is especially crucial for assisting system to understand users queries so as to classify the queries accurately to improve users satisfaction.Since the posted queries are usually short and lack of context,conventional methods heavily relying on query n-grams or other common features are not sufficient enough.This paper proposes a compact yet effective user intention classification method named as ST-UIC based on a constructed semantic tag repository.The method proposes to use a combination of four kinds of features including characters,non-key-noun part-of-speech tags,target words,and semantic tags.The experiments are based on a widely applied dataset provided by the First Evaluation of Chinese Human-Computer Dialogue Technology.The result shows that the method achieved a F1 score of 0.945,exceeding a list of baseline methods and demonstrating its effectiveness in user intent classification.

关键词： User intent Classification Target Words Semantic Tag

作者: Wenxiu Xie Dongfa Gao Ruoyao Ding Tianyong Hao

作者单位: School of Information Science and Technology,Guangdong University of Foreign Studies,Guangzhou,China School of Computer Science,South China Normal University,Guangzhou,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 224-234

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）