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
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–53
Language:
URL:
https://www.aclweb.org/anthology/W18-2907
DOI:
10.18653/v1/W18-2907
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-2907.pdf