M2L at SemEval-2016 task 8: AMR parsing with neural networks

Yevgeniy Puzikov; Daisuke Kawahara; Sadao Kurohashi

doi:10.18653/v1/s16-1178

M2L at SemEval-2016 task 8: AMR parsing with neural networks

Yevgeniy Puzikov, Daisuke Kawahara, Sadao Kurohashi

School of Fundamental Science and Engineering

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

5 Citations (Scopus)

Abstract

This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representation parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with additions that utilize a Feedforward Neural Network classifier and an enriched feature set. We observed that exploiting Neural Networks in Meaning Representation parsing is challenging and we could not benefit from it, while the feature enhancements yielded an improved performance over the baseline model.

Original language	English
Title of host publication	SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
Publisher	Association for Computational Linguistics (ACL)
Pages	1154-1159
Number of pages	6
ISBN (Electronic)	9781941643952
DOIs	https://doi.org/10.18653/v1/s16-1178
Publication status	Published - 2016
Event	10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States Duration: 2016 Jun 16 → 2016 Jun 17

Publication series

Name	SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

Conference

Conference	10th International Workshop on Semantic Evaluation, SemEval 2016
Country	United States
City	San Diego
Period	16/6/16 → 16/6/17

ASJC Scopus subject areas

Theoretical Computer Science
Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.18653/v1/s16-1178

Fingerprint Dive into the research topics of 'M2L at SemEval-2016 task 8: AMR parsing with neural networks'. Together they form a unique fingerprint.

Cite this

Puzikov, Y., Kawahara, D., & Kurohashi, S. (2016). M2L at SemEval-2016 task 8: AMR parsing with neural networks. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 1154-1159). (SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1178

M2L at SemEval-2016 task 8 : AMR parsing with neural networks. / Puzikov, Yevgeniy; Kawahara, Daisuke; Kurohashi, Sadao.

SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings. Association for Computational Linguistics (ACL), 2016. p. 1154-1159 (SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings).

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

Puzikov, Y, Kawahara, D & Kurohashi, S 2016, M2L at SemEval-2016 task 8: AMR parsing with neural networks. in SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings. SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings, Association for Computational Linguistics (ACL), pp. 1154-1159, 10th International Workshop on Semantic Evaluation, SemEval 2016, San Diego, United States, 16/6/16. https://doi.org/10.18653/v1/s16-1178

@inproceedings{7d28b60ae41847ecbbbd406e7f585e85,

title = "M2L at SemEval-2016 task 8: AMR parsing with neural networks",

abstract = "This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representation parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with additions that utilize a Feedforward Neural Network classifier and an enriched feature set. We observed that exploiting Neural Networks in Meaning Representation parsing is challenging and we could not benefit from it, while the feature enhancements yielded an improved performance over the baseline model.",

author = "Yevgeniy Puzikov and Daisuke Kawahara and Sadao Kurohashi",

note = "Funding Information: We thank the anonymous reviewers for their insightful comments and suggestions. The first author is supported by a Japanese Government (MEXT) research scholarship.; 10th International Workshop on Semantic Evaluation, SemEval 2016 ; Conference date: 16-06-2016 Through 17-06-2016",

year = "2016",

doi = "10.18653/v1/s16-1178",

language = "English",

series = "SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings",

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

pages = "1154--1159",

booktitle = "SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings",

}

TY - GEN

T1 - M2L at SemEval-2016 task 8

T2 - 10th International Workshop on Semantic Evaluation, SemEval 2016

AU - Puzikov, Yevgeniy

AU - Kawahara, Daisuke

AU - Kurohashi, Sadao

N1 - Funding Information: We thank the anonymous reviewers for their insightful comments and suggestions. The first author is supported by a Japanese Government (MEXT) research scholarship.

PY - 2016

Y1 - 2016

N2 - This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representation parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with additions that utilize a Feedforward Neural Network classifier and an enriched feature set. We observed that exploiting Neural Networks in Meaning Representation parsing is challenging and we could not benefit from it, while the feature enhancements yielded an improved performance over the baseline model.

AB - This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representation parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with additions that utilize a Feedforward Neural Network classifier and an enriched feature set. We observed that exploiting Neural Networks in Meaning Representation parsing is challenging and we could not benefit from it, while the feature enhancements yielded an improved performance over the baseline model.

UR - http://www.scopus.com/inward/record.url?scp=85035780563&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85035780563&partnerID=8YFLogxK

U2 - 10.18653/v1/s16-1178

DO - 10.18653/v1/s16-1178

M3 - Conference contribution

AN - SCOPUS:85035780563

T3 - SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

SP - 1154

EP - 1159

BT - SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

PB - Association for Computational Linguistics (ACL)

Y2 - 16 June 2016 through 17 June 2016

ER -