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.
%0 Generic
%1 nooralahzadeh2018syntactic
%A Nooralahzadeh, Farhad
%A Øvrelid, Lilja
%D 2018
%K 2018 classification dependency nlp nn relation semeval semeval18 tree
%T Syntactic Dependency Representations in Neural Relation Classification
%U http://arxiv.org/abs/1805.11461
%X 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.
@misc{nooralahzadeh2018syntactic,
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.},
added-at = {2018-09-13T10:31:41.000+0200},
author = {Nooralahzadeh, Farhad and Øvrelid, Lilja},
biburl = {https://www.bibsonomy.org/bibtex/2b53519086715bab81506641866971a93/schwemmlein},
interhash = {a8b8d2428f226f08a368d2915f67c4d7},
intrahash = {b53519086715bab81506641866971a93},
keywords = {2018 classification dependency nlp nn relation semeval semeval18 tree},
timestamp = {2018-09-13T10:31:41.000+0200},
title = {Syntactic Dependency Representations in Neural Relation Classification},
url = {http://arxiv.org/abs/1805.11461},
year = 2018
}