Neural Temporal Relation Extraction

Dmitriy Dligach, Timothy Miller, Chen Lin, Steven Bethard, Guergana Savova
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers  
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.
doi:10.18653/v1/e17-2118 dblp:conf/eacl/BethardMDLS17 fatcat:vobk2vw5yndqbjjq2t73ywsp2a