Emotion cause detection with linguistic constructions

Ying Chen; Yat Mei Lee; Shoushan Li; Chu-ren Huang

Emotion cause detection with linguistic constructions

Ying Chen, Yat Mei Lee, Shoushan Li, Chu-ren Huang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

172 Citations (Scopus)

Abstract

This paper proposes a multi-label approach to detect emotion causes. The multi-label model not only detects multi- clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we create two sets of linguistic patterns during feature extraction. Both manually generalized patterns and automatically generalized patterns are designed to extract general cause expressions or specific constructions for emotion causes. Experiments show that our system achieves a performance much higher than a baseline model.

Original language	English
Title of host publication	Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages	179-187
Number of pages	9
Publication status	Published - 1 Dec 2010
Event	23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: 23 Aug 2010 → 27 Aug 2010

Conference

Conference	23rd International Conference on Computational Linguistics, Coling 2010
Country/Territory	China
City	Beijing
Period	23/08/10 → 27/08/10

ASJC Scopus subject areas

Language and Linguistics
Computational Theory and Mathematics
Linguistics and Language

Cite this

@inproceedings{9dc05a113ed240c7942aeca1f40b3df1,

title = "Emotion cause detection with linguistic constructions",

abstract = "This paper proposes a multi-label approach to detect emotion causes. The multi-label model not only detects multi- clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we create two sets of linguistic patterns during feature extraction. Both manually generalized patterns and automatically generalized patterns are designed to extract general cause expressions or specific constructions for emotion causes. Experiments show that our system achieves a performance much higher than a baseline model.",

author = "Ying Chen and Lee, {Yat Mei} and Shoushan Li and Chu-ren Huang",

year = "2010",

month = dec,

day = "1",

language = "English",

pages = "179--187",

booktitle = "Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference",

note = "23rd International Conference on Computational Linguistics, Coling 2010 ; Conference date: 23-08-2010 Through 27-08-2010",

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Emotion cause detection with linguistic constructions. / Chen, Ying; Lee, Yat Mei; Li, Shoushan et al.
Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference. 2010. p. 179-187.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Emotion cause detection with linguistic constructions

AU - Chen, Ying

AU - Lee, Yat Mei

AU - Li, Shoushan

AU - Huang, Chu-ren

PY - 2010/12/1

Y1 - 2010/12/1

N2 - This paper proposes a multi-label approach to detect emotion causes. The multi-label model not only detects multi- clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we create two sets of linguistic patterns during feature extraction. Both manually generalized patterns and automatically generalized patterns are designed to extract general cause expressions or specific constructions for emotion causes. Experiments show that our system achieves a performance much higher than a baseline model.

AB - This paper proposes a multi-label approach to detect emotion causes. The multi-label model not only detects multi- clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we create two sets of linguistic patterns during feature extraction. Both manually generalized patterns and automatically generalized patterns are designed to extract general cause expressions or specific constructions for emotion causes. Experiments show that our system achieves a performance much higher than a baseline model.

UR - http://www.scopus.com/inward/record.url?scp=80053414827&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SP - 179

EP - 187

BT - Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference

T2 - 23rd International Conference on Computational Linguistics, Coling 2010

Y2 - 23 August 2010 through 27 August 2010

ER -

Emotion cause detection with linguistic constructions

Abstract

Conference

ASJC Scopus subject areas

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