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

Department of Electrical Engineering

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

44 Scopus citations

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
State	Published - 2014
Event	14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014 - Gothenburg, Sweden Duration: 26 Apr 2014 → 30 Apr 2014

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	26/04/14 → 30/04/14

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).

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).

@inproceedings{861757757dad4b7eb08fc8483889ca25,

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",

note = "Publisher Copyright: {\textcopyright} 2014 Association for Computational Linguistics; 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014 ; Conference date: 26-04-2014 Through 30-04-2014",

year = "2014",

language = "???core.languages.en\_GB???",

series = "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",

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

}

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, 26/04/14.

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 › peer-review

TY - GEN

T1 - Chinese Open Relation Extraction for Knowledge Acquisition

AU - Tseng, Yuen Hsien

AU - Lee, Lung Hao

AU - Lin, Shu Yen

AU - Liao, Bo Shun

AU - Liu, Mei Jun

AU - Chen, Hsin Hsi

AU - Etzioni, Oren

AU - Fader, Anthony

PY - 2014

Y1 - 2014

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/84923132098

M3 - 會議論文篇章

AN - SCOPUS:84923132098

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

SP - 12

EP - 16

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

PB - Association for Computational Linguistics (ACL)

T2 - 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014

Y2 - 26 April 2014 through 30 April 2014

ER -

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).

Chinese Open Relation Extraction for Knowledge Acquisition

Abstract

Publication series

Conference

Fingerprint

Cite this