Chinese Open Relation Extraction for Knowledge Acquisition

Yuen Hsien Tseng; Lung Hao Lee; Shu Yen Lin; Bo Shun Liao; Mei Jun Liu; Hsin Hsi Chen; Oren Etzioni; Anthony Fader

Chinese Open Relation Extraction for Knowledge Acquisition

Yuen Hsien Tseng, Lung Hao Lee, Shu Yen Lin, Bo Shun Liao, Mei Jun Liu, Hsin Hsi Chen, Oren Etzioni, Anthony Fader

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application. To our best knowledge, CORE is the first Chinese Open IE system for knowledge acquisition.

Original language	English
Title of host publication	EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Publisher	Association for Computational Linguistics (ACL)
Pages	12-16
Number of pages	5
ISBN (Electronic)	9781937284992
Publication status	Published - 2014
Event	14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014 - Gothenburg, Sweden Duration: 2014 Apr 26 → 2014 Apr 30

Publication series

Name	EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference	14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014
Country/Territory	Sweden
City	Gothenburg
Period	2014/04/26 → 2014/04/30

ASJC Scopus subject areas

Computational Theory and Mathematics
Software
Linguistics and Language

Cite this

Tseng, Y. H., Lee, L. H., Lin, S. Y., Liao, B. S., Liu, M. J., Chen, H. H., Etzioni, O., & Fader, A. (2014). Chinese Open Relation Extraction for Knowledge Acquisition. In EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 12-16). (EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference). Association for Computational Linguistics (ACL).

Chinese Open Relation Extraction for Knowledge Acquisition. / Tseng, Yuen Hsien; Lee, Lung Hao; Lin, Shu Yen et al.
EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2014. p. 12-16 (EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tseng, YH, Lee, LH, Lin, SY, Liao, BS, Liu, MJ, Chen, HH, Etzioni, O & Fader, A 2014, Chinese Open Relation Extraction for Knowledge Acquisition. in EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 12-16, 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, Gothenburg, Sweden, 2014/04/26.

Tseng YH, Lee LH, Lin SY, Liao BS, Liu MJ, Chen HH et al. Chinese Open Relation Extraction for Knowledge Acquisition. In EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2014. p. 12-16. (EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference).

Tseng, Yuen Hsien ; Lee, Lung Hao ; Lin, Shu Yen et al. / Chinese Open Relation Extraction for Knowledge Acquisition. EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2014. pp. 12-16 (EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference).

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title = "Chinese Open Relation Extraction for Knowledge Acquisition",

abstract = "This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application. To our best knowledge, CORE is the first Chinese Open IE system for knowledge acquisition.",

author = "Tseng, {Yuen Hsien} and Lee, {Lung Hao} and Lin, {Shu Yen} and Liao, {Bo Shun} and Liu, {Mei Jun} and Chen, {Hsin Hsi} and Oren Etzioni and Anthony Fader",

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AU - Liu, Mei Jun

AU - Chen, Hsin Hsi

AU - Etzioni, Oren

AU - Fader, Anthony

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AB - This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application. To our best knowledge, CORE is the first Chinese Open IE system for knowledge acquisition.

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M3 - Conference contribution

AN - SCOPUS:84923132098

T3 - EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

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BT - EACL 2014 - 14th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

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ER -

Chinese Open Relation Extraction for Knowledge Acquisition

Abstract

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Conference

ASJC Scopus subject areas

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