Syntactic Dependency Representations in Neural Relation Classification

Farhad Nooralahzadeh, Lilja Øvrelid


Abstract
We investigate the use of different syntactic dependency representations in a neural relation classification task and compare the CoNLL, Stanford Basic and Universal Dependencies schemes. We further compare with a syntax-agnostic approach and perform an error analysis in order to gain a better understanding of the results.
Anthology ID:
W18-2907
Volume:
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Georgiana Dinu, Miguel Ballesteros, Avirup Sil, Sam Bowman, Wael Hamza, Anders Sogaard, Tahira Naseem, Yoav Goldberg
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–53
Language:
URL:
https://aclanthology.org/W18-2907
DOI:
10.18653/v1/W18-2907
Bibkey:
Cite (ACL):
Farhad Nooralahzadeh and Lilja Øvrelid. 2018. Syntactic Dependency Representations in Neural Relation Classification. In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP, pages 47–53, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Syntactic Dependency Representations in Neural Relation Classification (Nooralahzadeh & Øvrelid, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2907.pdf
Data
Universal Dependencies