Sentence Topics Based Knowledge Acquisition for Question Answering

Hyo-Jung OH  Bo-Hyun YUN  

IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.4   pp.969-975
Publication Date: 2008/04/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.4.969
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Knowledge-Based Software Engineering)
Category: Knowledge Engineering
knowledge acquisition,  machine learning,  question answering,  

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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.