TY - GEN
T1 - Improving a strong neural parser with conjunction-specific features
AU - Ficler, Jessica
AU - Goldberg, Yoav
N1 - Publisher Copyright:
© 2017 Association for Computational Linguistics.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.
AB - While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.
UR - https://www.scopus.com/pages/publications/85021635908
U2 - 10.18653/v1/e17-2055
DO - 10.18653/v1/e17-2055
M3 - Conference contribution
AN - SCOPUS:85021635908
T3 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
SP - 343
EP - 348
BT - Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Y2 - 3 April 2017 through 7 April 2017
ER -