Abstract
Neural network methods based on distant supervision has been widely used in studies concerning relation extraction, however, a traditional convolutional neural network can not effectively extract the dependency relationship and structured information between words in sentences. In order to solve this problem, we propose a novel approach to improve relation extraction results. Specifically, we propose to first apply a neural network-based model to encode sentences, feature vectors obtained are then fed into a one-dimensional dilated convolutional neural network to extract the relation. Finally, sentence-level attention mechanism is used to reduce the noise caused by the mislabeling problem of distant supervision. Our approach has been evaluated on real world datasets NYT10 and compared with a wide range of baselines. Experimental results show that: (1) our approach can improve the performance of neural network relation extraction based on distant supervision; (2) the proposed approach achieves outstanding results on the datasets.
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Acknowledgement
This work was supported by the Natural Science Foundation of Heilongjiang Province (No. LH2020F043, JJ2019LH1096,F2018028), the Scientific Research Fund of Heilongjiang University (No. 2020-KYYWF-1010).
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Wang, K., Ren, Q., Hui, L., Xu, H., Li, S., Xu, P. (2021). Improving Relation Extraction via Joint Coding Using BiLSTM and DCNN. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_52
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