A maximum entropy model based answer extraction for chinese question answering

Authors:
Ang Sun;Minghu Jiang;Yanjun Ma
Affiliations:
Computational Linguistics Lab, Dept. of Chinese Language, Tsinghua University, Beijing, China;Computational Linguistics Lab, Dept. of Chinese Language, Tsinghua University, Beijing, China;Computational Linguistics Lab, Dept. of Chinese Language, Tsinghua University, Beijing, China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

We regard answer extraction of Question Answering (QA) system as a classification problem, classifying answer candidate sentences into positive or negative. To confirm the feasibility of this new approach, we first extract features concerning question sentences and answer words from question answer pairs (QA pair), then we conduct experiments based on these features, using Maximum Entropy Model (MEM) as a Machine Learning (ML) technique. The first experiment conducted on the class-TIME_YEAR achieves 81.24% in precision and 78.48% in recall. The second experiment expanded to two other classes-OBJ_SUBSTANCE and LOC_CONTINENT also shows good performance.