Neural Temporal Relation Extraction

Citation:

Dligach D, Miller T, Lin C, Bethard S, Savova G. Neural Temporal Relation Extraction, in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Valencia, Spain: Association for Computational Linguistics ; 2017 :746–751.

Date Presented:

April

Abstract:

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-of-the-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.

Website