Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Relation Extraction NYT NovelTagging F1 42.0 # 20
Relation Extraction NYT11-HRL NovelTagging F1 47.9 # 10
Relation Extraction NYT-single NovelTagging F1 49.5 # 3
Relation Extraction WebNLG NovelTagging F1 28.3 # 14


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