Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data

Eliyahu Kiperwasser; Yoav Goldberg

doi:10.18653/v1/d15-1158

Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data

Eliyahu Kiperwasser, Yoav Goldberg

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

8 Scopus citations

Abstract

We present a semi-supervised approach to improve dependency parsing accuracy by using bilexical statistics derived from auto-parsed data. The method is based on estimating the attachment potential of head-modifier words, by taking into account not only the head and modifier words themselves, but also the words surrounding the head and the modifier. When integrating the learned statistics as features in a graph-based parsing model, we observe nice improvements in accuracy when parsing various English datasets.

Original language	English
Title of host publication	Conference Proceedings - EMNLP 2015
Subtitle of host publication	Conference on Empirical Methods in Natural Language Processing
Publisher	Association for Computational Linguistics (ACL)
Pages	1348-1353
Number of pages	6
ISBN (Electronic)	9781941643327
DOIs	https://doi.org/10.18653/v1/d15-1158
State	Published - 1 Jan 2015
Externally published	Yes
Event	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal Duration: 17 Sep 2015 → 21 Sep 2015

Publication series

Name	Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Conference

Conference	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015
Country/Territory	Portugal
City	Lisbon
Period	17/09/15 → 21/09/15

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Access to Document

10.18653/v1/d15-1158

Cite this

Kiperwasser, E., & Goldberg, Y. (2015). Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 1348-1353). (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1158

Kiperwasser, Eliyahu ; Goldberg, Yoav. / Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2015. pp. 1348-1353 (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing).

@inproceedings{ec76532a353c4c3d8e5030b63031b536,

title = "Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data",

abstract = "We present a semi-supervised approach to improve dependency parsing accuracy by using bilexical statistics derived from auto-parsed data. The method is based on estimating the attachment potential of head-modifier words, by taking into account not only the head and modifier words themselves, but also the words surrounding the head and the modifier. When integrating the learned statistics as features in a graph-based parsing model, we observe nice improvements in accuracy when parsing various English datasets.",

author = "Eliyahu Kiperwasser and Yoav Goldberg",

note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 ; Conference date: 17-09-2015 Through 21-09-2015",

year = "2015",

month = jan,

day = "1",

doi = "10.18653/v1/d15-1158",

language = "English",

series = "Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing",

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

pages = "1348--1353",

booktitle = "Conference Proceedings - EMNLP 2015",

address = "United States",

}

Kiperwasser, E & Goldberg, Y 2015, Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data. in Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (ACL), pp. 1348-1353, Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17/09/15. https://doi.org/10.18653/v1/d15-1158

Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data. / Kiperwasser, Eliyahu; Goldberg, Yoav.
Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2015. p. 1348-1353 (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing).

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

TY - GEN

T1 - Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data

AU - Kiperwasser, Eliyahu

AU - Goldberg, Yoav

PY - 2015/1/1

Y1 - 2015/1/1

N2 - We present a semi-supervised approach to improve dependency parsing accuracy by using bilexical statistics derived from auto-parsed data. The method is based on estimating the attachment potential of head-modifier words, by taking into account not only the head and modifier words themselves, but also the words surrounding the head and the modifier. When integrating the learned statistics as features in a graph-based parsing model, we observe nice improvements in accuracy when parsing various English datasets.

AB - We present a semi-supervised approach to improve dependency parsing accuracy by using bilexical statistics derived from auto-parsed data. The method is based on estimating the attachment potential of head-modifier words, by taking into account not only the head and modifier words themselves, but also the words surrounding the head and the modifier. When integrating the learned statistics as features in a graph-based parsing model, we observe nice improvements in accuracy when parsing various English datasets.

UR - https://www.scopus.com/pages/publications/84959875045

U2 - 10.18653/v1/d15-1158

DO - 10.18653/v1/d15-1158

M3 - Conference contribution

AN - SCOPUS:84959875045

T3 - Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

SP - 1348

EP - 1353

BT - Conference Proceedings - EMNLP 2015

PB - Association for Computational Linguistics (ACL)

T2 - Conference on Empirical Methods in Natural Language Processing, EMNLP 2015

Y2 - 17 September 2015 through 21 September 2015

ER -

Kiperwasser E, Goldberg Y. Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL). 2015. p. 1348-1353. (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). doi: 10.18653/v1/d15-1158

Semi-supervised dependency parsing using bilexical contextual features from auto-parsed data

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this