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A survey on neural relation extraction

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Abstract

Relation extraction is a key task for knowledge graph construction and natural language processing, which aims to extract meaningful relational information between entities from plain texts. With the development of deep learning, many neural relation extraction models were proposed recently. This paper introduces a survey on the task of neural relation extraction, including task description, widely used evaluation datasets, metrics, typical methods, challenges and recent research progresses. We mainly focus on four recent research problems: (1) how to learn the semantic representations from the given sentences for the target relation, (2) how to train a neural relation extraction model based on insufficient labeled instances, (3) how to extract relations across sentences or in a document and (4) how to jointly extract relations and corresponding entities? Finally, we give out our conclusion and future research issues.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Kang Liu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Kang Liu

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Correspondence to Kang Liu.

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This work was supported by the National Natural Science Foundation of China (Grant Nos. 61922085 and 61533018), the Natural Key R&D Program of China (Grant No. 2018YFC0830101), the Key Research Program of the Chinese Academy of Sciences (Grant No. ZDBS-SSW-JSC006), Beijing Academy of Artificial Intelligence (BAAI2019QN0301), the Open Project of Beijing Key Laboratory of Mental Disorders (2019JSJB06), and the independent research project of National Laboratory of Pattern Recognition.

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Liu, K. A survey on neural relation extraction. Sci. China Technol. Sci. 63, 1971–1989 (2020). https://doi.org/10.1007/s11431-020-1673-6

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