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

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.