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Question Classification in English-Chinese Cross-Language Question Answering: An Integrated Genetic Algorithm and Machine Learning Approach

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3 Author(s)
Min-Yuh Day ; Institute of Information Science, Academia Sinica, Taiwan; Department of Information Management, National Taiwan University, Taiwan. myday@iis.sinica.edu.tw ; Chorng-Shyong Ong ; Wen-Lian Hsu

Question classification plays an important role in cross-language question answering (CLQA) systems, while question Informer plays a key role in enhancing question classification for factual question answering. In this paper, we propose an integrated genetic algorithm (GA) and machine learning (ML) approach for question classification in English-Chinese cross-language question answering. To enhance question informer prediction, we use a hybrid method that integrates GA and conditional random fields (CRF) to optimize feature subset selection in a CRF-based question informer prediction model. The proposed approach extends cross-language question classification by using the GA-CRF question informer feature with support vector machines (SVM). The results of evaluations on the NTCIR-6 CLQA question sets demonstrate the efficacy of the approach in improving the accuracy of question classification in English-Chinese cross-language question answering.

Published in:

2007 IEEE International Conference on Information Reuse and Integration

Date of Conference:

13-15 Aug. 2007