Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Special Section on Knowledge-Based Software Engineering -- Papers -- Knowledge Engineering |
Sentence Topics Based Knowledge Acquisition for Question Answering
1 The author is with ETRI, 161 Gajeong-dong, Yuseong-gu, Daejeon, 350–700, Korea., 2 The author is with Mokwon University, Mokwon Gil 21, Seo-gu, Daejeon, 302–318, Korea. E-mail: ybh{at}mokwon.ac.kr
This paper presents a knowledge acquisition method using sentence topics for question answering. We define templates for information extraction by the Korean concept network semi-automatically. Moreover, we propose the two-phase information extraction model by the hybrid machine learning such as maximum entropy and conditional random fields. In our experiments, we examined the role of sentence topics in the template-filling task for information extraction. Our experimental result shows the improvement of 18% in F-score and 434% in training speed over the plain CRF-based method for the extraction task. In addition, our result shows the improvement of 8% in F-score for the subsequent QA task.
Key Words: knowledge acquisition, machine learning, question answering
Manuscript received July 2, 2007. Manuscript revised September 28, 2007.
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