Chunking-based Question Type Identification for Multi-Sentence Queries
Mineki Takechi, Takenobu Tokunaga, Yūji Matsumoto · 2007
This paper describes a technique of question type identification for multi-sentence queries in open domain questionanswering. Based on observations of queries in real questionanswering services on the Web, we propose a method to decompose a multi-sentence query into question items and to identify their question types. The proposed method is an efficient sentence-chunking based technique by using a machine learning method, namely Conditional Random Fields. Our method can handle a multisentence query comprising multiple question items, as well as traditional single sentence queries in the same framework. Based on the evaluation results, we discuss possible enhancement to improve the accuracy and robustness.