Abstract
Current distant supervised relation extraction algorithms based on neural network deep learning always label a large number of irrelevant sentences as valid data or sentences as wrong relation because of their overly broad assumptions. Although many scholars have proposed some methods to reduce the influence of distant supervision noise and their optimization methods, such as organizing sentences into bag of sentences and introducing attention mechanism, they still cannot obtain a good feature vector of a bag of sentences. In this paper, we propose an attention-weight allocation algorithm for sentences inside a bag of sentences based on relative alignment. Compared with previous methods, it can better capture the similarity of sentences to relative vectors, and better extract relationships in sentences under distant supervision. On the standard dataset, the proposed model has improved by 2.5%, 2.4%, 2.8% and 2% compared with the PCNN_ATT model, P@100, P@200, P@300 and AUC indexes, respectively.
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Acknowledgement
The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. The work is supported by Science and Technology Project of the State Grid Information & Telecommunication Branch, “The research and technology for multi-Scenario Oriented security protection framework” (52993920002J).
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Li, J., Huang, X., Gao, Y., Liu, J., Zhang, R., Zhao, J. (2022). Distant Supervised Relation Extraction Based on Sentence-Level Attention with Relation Alignment. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_12
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DOI: https://doi.org/10.1007/978-3-031-06794-5_12
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