Mining redundancy in candidate-bearing snippets to improve web question answering

Authors:
Youzheng Wu;Xinhui Hu;Hideki Kashioka
Affiliations:
National Institute of Information and Communications Technology (NICT), Kyoto, Japan;National Institute of Information and Communications Technology (NICT), Kyoto, Japan;National Institute of Information and Communications Technology (NICT), Kyoto, Japan
Venue:
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Year:
2007

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Abstract

Conventional question answering (QA) techniques independently process candidate-bearing snippets to select an exact answer to a question from candidate answers. This paper presents two novel ways of utilizing redundancy in candidate-bearing snippets to help select an exact answer to a question in our Web QA system, i.e., cluster-based language model (CLM-M) and unsupervised SVM classifier (U-SVM) techniques. The comparative experiments demonstrate that the proposed methods significantly outperform the language model-based (LM-M) and supervised SVM-based (S-SVM) techniques that do not utilize this redundancy in the candidate-bearing snippets. Using the CLM-M, the top_1 score is increased from 36.03% (LM-M) to 46.96%; and the top_1 improvement in the U-SVM over the S-SVM is about 23%. Moreover, a cross-model comparison shows that the performance ranking of these models is: U-SVM CLM-LM LM-M S-SVM R-M (the retrieval-based model).