Sentence Topics Based Knowledge Acquisition for Question Answering

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 max...

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Veröffentlicht in:	IEICE Transactions on Information and Systems 2008/04/01, Vol.E91.D(4), pp.969-975
Hauptverfasser:	OH, Hyo-Jung, YUN, Bo-Hyun
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Sprache:	eng
Schlagworte:	Extraction Knowledge acquisition Machine learning Mathematical models Maximum entropy Networks question answering Sentences Tasks
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container_end_page	975
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container_title	IEICE Transactions on Information and Systems
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creator	OH, Hyo-Jung YUN, Bo-Hyun
description	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.
doi_str_mv	10.1093/ietisy/e91-d.4.969
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subjects	Extraction Knowledge acquisition Machine learning Mathematical models Maximum entropy Networks question answering Sentences Tasks
title	Sentence Topics Based Knowledge Acquisition for Question Answering
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