基于双向译码的实体关系抽取方法

doi:10.3969/j.issn.0255-8297.2025.03.010

Abstract

Abstract: To address the challenges of error propagation, overlapping triple problem and subject-object alignment in the existing relationship triplet extraction methods, this study proposes a novel bidirectional translation and decoding model. The model reframes the extraction process into three sub-tasks: entity extraction, subject-object alignment and relationship judgment. The bidirectional structure effectively alleviates error propagation, while the translation and decoding method based on the attention mechanism deals with the overlapping triple problem and aligns the subject and object. Finally, a bipartite entity-torelationship diagram fully explores the relationship between entity pairs, enabling accurate relationship judgment. Experimental results on public datasets have validated the performance of the proposed model.

Key words: nature language processing, information extraction, entity extraction, relationship extraction, error propagation, triplet

CLC Number:

TP391.1
TP18

LIU Hui, ZHANG Zhi, CHEN Yupeng. Entity Relationship Extraction Method Based on Bidirectional Decoding[J]. Journal of Applied Sciences, 2025, 43(3): 491-503.

References

[1] Nayak T, Majumder N, Goyal P, et al. Deep neural approaches to relation triplets extraction: a comprehensive survey [J]. Cognitive Computation, 2021, 13(5): 1215-1232.
[2] Dong X L, Gabrilovich E, Heitz G, et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion [C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 601-610.
[3] Li X, Yin F, Sun Z, et al. Entity-relation extraction as multi-turn question answering [C]//57th Annual Meeting of the Association for Computational Linguistics, 2019: 1340-1350.
[4] 丁玉杨. 基于问答对关键词提取的问答系统研究[D]. 重庆: 重庆大学, 2021.
[5] 丁泽源, 杨志豪, 罗凌. 基于深度学习的中文生物医学实体关系抽取系统[J]. 中文信息学报, 2021, 35(5): 70-76. Ding Z Y, Yang Z H, Luo L. Chinese biomedical entity relation extraction system based on deep learning [J]. Journal of Chinese Information Processing, 2021, 35(5): 70-76. (in Chinese)
[6] 杨崇洛, 生龙, 魏忠诚. 新冠文本实体关系抽取及数据集构建方法研究[J]. 计算机工程与应用, 2023, 59(8): 8. Yang C L, Shen L, Wei Z C. Research on COVID-19 text entity relation extraction and dataset construction methods [J]. Computer Engineering and Applications, 2023, 59(8): 8. (in Chinese)
[7] Zhou G, Su J, Zhang J, et al. Exploring various knowledge in relation extraction [C]//43rd Annual Meeting of the Association for Computational Linguistics, 2005: 427-434.
[8] Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction [J]. The Journal of Machine Learning Research, 2003, 3: 1083-1106.
[9] Tjong Kim Sang E F, Meulder F D. Introduction to the CoNLL-2003 shared task: languageindependent named entity recognition [C]//Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, 2003, 4: 142-147.
[10] 薛鹏飞, 沈毅, 胡淼. 基于规则的域名WHOIS信息抽取技术研究[J]. 信息对抗技术, 2023, 2(1): 66-77. Xue P F, Shen Y, Hu M. Research on rule based domain WHOIS information extraction technology [J]. Information Adversarial Technology, 2023, 2(1): 66-77. (in Chinese)
[11] Bunescu R C, Mooney R J. A shortest path dependency kernel for relation extraction [C]//Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, 2005: 724-731.
[12] Wei Z, Su J, Wang Y, et al. A novel cascade binary tagging framework for relational triple extraction [C]//58th Annual Meeting of the Association for Computational Linguistics, 2020: 1476-1488.
[13] Li X, Luo X, Dong C, et al. TDEER: an efficient translating decoding schema for joint extraction of entities and relations [C]//2021 Conference on Empirical Methods in Natural Language Processing, 2021: 8055-8064.
[14] Zheng H, Wen R, Chen X, et al. PRGC: potential relation and global correspondence based joint relational triple extraction [C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 6225-6235.
[15] Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding [C]//2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[16] Pennington J, Socher R, Manning C. GloVe: global vectors for word representation [C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014: 1532-1543.
[17] Liu Y H, Ott M, Goyal N, et al. RoBERTa: a robustly optimized BERT pretraining approach [DB/OL]. (2019-07-26) [2024-01-30]. https://arxiv.org/abs/1907.11692.
[18] Sui D, Zeng X, Chen Y, et al. Joint entity and relation extraction with set prediction networks [J]. IEEE Transactions on Neural Networks and Learning Systems, 2023: 1-12.
[19] Yu B W, Zhang Z Y, Shu X B, et al. Joint extraction of entities and relations based on a novel decomposition strategy [DB/OL]. (2020-02-19) [2024-01-30]. https://arxiv.org/abs/1909.04273.
[20] Wang Y, Yu B, Zhang Y, et al. TPLinker: single-stage joint extraction of entities and relations through token pair linking [C]//28th International Conference on Computational Linguistics, 2020: 1572-1582.
[21] Riedel S, Yao L M, Mccallum A. Modeling relations and their mentions without labeled text [C]//European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010: 148-163.
[22] Gardent C, Shimorina A, Narayan S, et al. Creating training corpora for NLG microplanners [C]//55th Annual Meeting of the Association for Computational Linguistics 2017: 179- 188.
[23] Zheng S, Wang F, Bao H, et al. Joint extraction of entities and relations based on a novel tagging scheme [C]//55th Annual Meeting of the Association for Computational Linguistics, 2017: 1227-1236.
[24] Zeng X, Zeng D, He S, et al. Extracting relational facts by an end-to-end neural model with copy mechanism [C]//56th Annual Meeting of the Association for Computational Linguistics, 2018: 506-514.
[25] Bekoulis G, Deleu J, Demeester T, et al. Joint entity recognition and relation extraction as a multi-head selection problem [J]. Expert Systems with Applications, 2018, 114: 34-45.
[26] Fu T J, Li P H, Ma W Y. GraphRel: modeling text as relational graphs for joint entity and relation extraction [C]//57th Annual Meeting of the Association for Computational Linguistics, 2019: 1409-1418.
[27] Zeng X R, He S Z, Zeng D J, et al. Learning the extraction order of multiple relational facts in a sentence with reinforcement learning [C]//2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 367-377.
[28] Yuan Y, Zhou X F, Pan S R, et al. A relation-specific attention network for joint entity and relation extraction [C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020: 4054-4060.
[29] Kingma D P, Ba J. Adam: a method for stochastic optimization [DB/OL]. (2014-12-22) [2024- 01-30]. https://arxiv.org/abs/1412.6980.
[30] Chen Y B, Zhang Y Q, Hu C R, et al. Jointly extracting explicit and implicit relational triples with reasoning pattern enhanced binary pointer network [C]//2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021: 5694-5703.

Entity Relationship Extraction Method Based on Bidirectional Decoding

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