Sentence Topics Based Knowledge Acquisition for Question Answering

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2008-04-01

著者

• Yun Bo-hyun

  Mokwon University

• OH Hyo-Jung

  ETRI

関連論文

• Sentence Topics Based Knowledge Acquisition for Question Answering

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