Improving Chinese semantic role labeling using high-quality surface and deep case frames

Gongye Jin; Daisuke Kawahara; Sadao Kurohashi

doi:10.18653/v1/e17-1054

Improving Chinese semantic role labeling using high-quality surface and deep case frames

Gongye Jin, Daisuke Kawahara, Sadao Kurohashi

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

Abstract

This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.

Original language	English
Title of host publication	Long Papers
Publisher	Association for Computational Linguistics (ACL)
Pages	568-577
Number of pages	10
ISBN (Electronic)	9781510838604
DOIs	https://doi.org/10.18653/v1/e17-1054
Publication status	Published - 2017
Externally published	Yes
Event	15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain Duration: 2017 Apr 3 → 2017 Apr 7

Publication series

Name	15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Volume	1

Conference

Conference	15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Country/Territory	Spain
City	Valencia
Period	17/4/3 → 17/4/7

ASJC Scopus subject areas

Linguistics and Language
Language and Linguistics

Access to Document

10.18653/v1/e17-1054

Cite this

Jin, G., Kawahara, D., & Kurohashi, S. (2017). Improving Chinese semantic role labeling using high-quality surface and deep case frames. In Long Papers (pp. 568-577). (15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference; Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-1054

Improving Chinese semantic role labeling using high-quality surface and deep case frames. / Jin, Gongye; Kawahara, Daisuke; Kurohashi, Sadao.
Long Papers. Association for Computational Linguistics (ACL), 2017. p. 568-577 (15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference; Vol. 1).

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

Jin, G, Kawahara, D & Kurohashi, S 2017, Improving Chinese semantic role labeling using high-quality surface and deep case frames. in Long Papers. 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, vol. 1, Association for Computational Linguistics (ACL), pp. 568-577, 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, 17/4/3. https://doi.org/10.18653/v1/e17-1054

@inproceedings{8532e74509104b548ebf015310368413,

title = "Improving Chinese semantic role labeling using high-quality surface and deep case frames",

abstract = "This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.",

author = "Gongye Jin and Daisuke Kawahara and Sadao Kurohashi",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics.; 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 ; Conference date: 03-04-2017 Through 07-04-2017",

year = "2017",

doi = "10.18653/v1/e17-1054",

language = "English",

series = "15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference",

publisher = "Association for Computational Linguistics (ACL)",

pages = "568--577",

booktitle = "Long Papers",

}

TY - GEN

T1 - Improving Chinese semantic role labeling using high-quality surface and deep case frames

AU - Jin, Gongye

AU - Kawahara, Daisuke

AU - Kurohashi, Sadao

PY - 2017

Y1 - 2017

N2 - This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.

AB - This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.

UR - http://www.scopus.com/inward/record.url?scp=85021683066&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021683066&partnerID=8YFLogxK

U2 - 10.18653/v1/e17-1054

DO - 10.18653/v1/e17-1054

M3 - Conference contribution

AN - SCOPUS:85021683066

T3 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference

SP - 568

EP - 577

BT - Long Papers

PB - Association for Computational Linguistics (ACL)

T2 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

Y2 - 3 April 2017 through 7 April 2017

ER -

Improving Chinese semantic role labeling using high-quality surface and deep case frames

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this