Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision

Jianhao Yan, Lin He, Ruqin Huang, Jian Li, Ying Liu
2019 Proceedings of the 2019 Conference of the North  
Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores the
more » ... cally ignores the temporal relations among those supervising instances. This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot. For this purpose, we construct a dataset called WIKI-TIME 1 which additionally includes the valid period of a certain relation of two entities in the knowledge base. We propose a novel neural model to incorporate both the temporal information encoding and sequential reasoning. The experimental results show that, compared with the best of existing models, our model achieves better performance in both WIKI-TIME dataset and the well-studied NYT-10 dataset.
doi:10.18653/v1/n19-1107 dblp:conf/naacl/YanHHLL19 fatcat:nldkpb5purfupo46rsjbxp7a2a