TY - GEN
T1 - Neural temporal relation extraction
AU - Dligach, Dmitriy
AU - Miller, Timothy
AU - Lin, Chen
AU - Bethard, Steven
AU - Savova, Guergana
N1 - Publisher Copyright:
© 2017 Association for Computational Linguistics.
PY - 2017
Y1 - 2017
N2 - We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-ofthe- art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.
AB - We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-ofthe- art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.
UR - https://www.scopus.com/pages/publications/85021741627
UR - https://www.scopus.com/pages/publications/85021741627#tab=citedBy
U2 - 10.18653/v1/e17-2118
DO - 10.18653/v1/e17-2118
M3 - Conference contribution
AN - SCOPUS:85021741627
T3 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
SP - 746
EP - 751
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 -