A template alignment algorithm for question classification

Question classification (QC) plays a key role in automated question answering (QA) systems. In Chinese QC, for example, a question is analyzed and then labeled with the question type it belongs to and the expected answer type. In this paper, we propose a novel method of Chinese QC that integrates syntactic tags and semantic tags into an alignment-based approach. We adopt a template alignment (TA) algorithm to process large collections of Chinese questions and compare the classification results with those of INFOMAP, a human annotated knowledge inference engine for Chinese questions. We experimented with two approaches for the proposed system: a majority algorithm and a machine learning method that uses Support Vector Machine (SVM). The TA algorithm performs well with both approaches. The experimental results show that the accuracy achieved by TA (85.5%) is comparable to that of INFOMAP (88%). In contrast, QC based on the SVM approach, which incorporates syntactic features and TA yields an accuracy rate of 91.5%.