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Improving distant supervision relation extraction with entity-guided enhancement feature

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Abstract

Selective attention in distant supervision extraction relation is advantageous to deal with incorrectly labeled sentences in a bag, but it does not help in cases where many sentence bags consist of only one sentence. To resolve the deficiencies, we propose an entity-guided enhancement feature neural network for distant supervision relation extraction. We discover that key relation features are typically found in both significant words and phrases, which can be captured by entity guidance. We first develop an entity-directed attention that measures the relevance between entities and two levels of semantic units from word and phrase to capture reliable relation features, which are used to enhance the entity representations. Furthermore, two multi-level augmented entity representations are transformed to a relation representation via a linear layer. Then we adopt a semantic fusion layer to fuse multiple semantic representations such as the sentence representation encoded by piecewise convolutional neural network, two multi-level augmented entity representations, and the relation representation to get final enhanced sentence representation. Finally, with the guidance of the relation representations, we introduce a gate pooling strategy to generate a bag-level representation and address the one-sentence bag problem occurring in selective attention. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods.

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Data availability

Data available on request from the authors.

Notes

  1. http://iesl.cs.umass.edu/riedel/ecml/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Contract 62062012 and Contract 61967003, the Natural Science Foundation of Guangxi of China under Contract 2020GXNSFAA159082 and the Innovation Project of School of Computer Science and Information Engineering, Guangxi Normal University under Contract JXXYYJSCXXM-005.

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Haixu Wen & Xinhua Zhu

  2. Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Xinhua Zhu

  3. Faculty of Education, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Lanfang Zhang

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  1. Haixu Wen
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  2. Xinhua Zhu
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Correspondence to Xinhua Zhu.

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Wen, H., Zhu, X. & Zhang, L. Improving distant supervision relation extraction with entity-guided enhancement feature. Neural Comput & Applic 35, 7547–7560 (2023). https://doi.org/10.1007/s00521-022-08051-1

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