Abstract
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.
Highlights
Joint extraction of entities and relations is to detect entity mentions and recognize their semantic relations simultaneously from unstructured text, as Figure 1 shows
Different from open information extraction (Open IE) (Banko et al, 2007) whose relation words are extracted from the given sentence, in this task, relation words are extracted from a predefined relation set which may not appear in the given sentence
We propose a tagging scheme accompanied with the end-to-end model to settle this problem
Summary
Joint extraction of entities and relations is to detect entity mentions and recognize their semantic relations simultaneously from unstructured text, as Figure 1 shows. It is an important issue in knowledge extraction and automatic construction of knowledge base
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