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

Yevgeniy Puzikov, Daisuke Kawahara, Sadao Kurohashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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 languageEnglish
Title of host publicationSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages1154-1159
Number of pages6
ISBN (Electronic)9781941643952
Publication statusPublished - 2016
Externally publishedYes
Event10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States
Duration: 2016 Jun 162016 Jun 17

Publication series

NameSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

Conference

Conference10th International Workshop on Semantic Evaluation, SemEval 2016
CountryUnited States
CitySan Diego
Period16/6/1616/6/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Science Applications

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  • 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).