Author(s)

JiaBao Wang^*, ZhiBin Guo

Affiliation(s)

College of Cyber Security, Tarim University, Alar 843300, Xinjiang, China.

Corresponding Author

JiaBao Wang

ABSTRACT

Relation extraction is a core task in natural language processing, aiming to identify semantic relationships between entities in unstructured text. Existing relation extraction methods face significant challenges when dealing with overlapping triples, especially in Entity Pair Overlap (EPO) and Single Entity Overlap (SEO) scenarios. This paper proposes a Bi-directional Dynamic Gating Cascaded (B-DGC) relation extraction model, which is improved based on the CasRel model. The model first uses a BERT encoder to obtain text embeddings, then employs a fully connected neural network to simultaneously identify head and tail entities. It fuses text embeddings with head entity features from the head entity sequence to identify corresponding tail entities under specific relationships. Finally, a dynamic gating mechanism is used to fuse the tail entity recognition results from two stages. Experimental results on NYT and WebNLG datasets show that the B-DGC model significantly outperforms the baseline model CasRel in precision, recall, and F1-score, achieving 90.4%, 91.1%, and 90.7% on NYT, and 92.6%, 92.2%, and 92.4% on WebNLG. Additionally, the B-DGC model demonstrates superior performance across various overlapping triple types, confirming the effectiveness of the bi-directional verification and dynamic gating mechanism.

KEYWORDS

Relation extraction; Gating mechanism; Pre-trained model; Neural network

CITE THIS PAPER

JiaBao Wang, ZhiBin Guo. A BI-directional cascaded relation extraction model based on dynamic gating mechanism: an enhanced approach to overlapping triples. Journal of Computer Science and Electrical Engineering. 2025, 7(7): 49-54. DOI: https://doi.org/10.61784/jcsee3100.

REFERENCES

[1] Etzioni O, Banko M, Soderland S, et al. Open information extraction from the web. Communications of the ACM, 2005, 48(12): 68-74.

[2] Liu X, Zhang L. Neural relation extraction with multi-lingual attention. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017: 2660-2669.

[3] Zheng S, Wang F, Bao H, et al. Joint extraction of entities and relations based on a novel tagging scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017, 1: 1227-1236.

[4] Han X, Yu P, Liu Z, et al. A survey on neural relation extraction. IEEE Transactions on Knowledge and Data Engineering, 2019, 32(1): 5-29.

[5] Wei Z, Su D, Wang Y, et al. Casrel: A novel cascade binary tagging framework for relation extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 1950-1960.

[6] Zeng X, Zeng D, He S, et al. Extracting relational facts by an end-to-end neural model with copy mechanism. Proceedings of the 27th International Conference on Computational Linguistics, 2018: 5068-5078.

[7] Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction. Journal of Machine Learning Research, 2003, 3: 1083-1106.

[8] Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014: 2335-2344.

[9] dos Santos C N, Guimaraes V. Deep convolutional neural networks for relation classification. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015: 1635-1640.

[10] Miwa M, Sasaki Y. Modeling joint entity and relation extraction with table representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014: 1858-1869.

[11] Li Q, Ji H. Incremental joint extraction of entity mentions and relations. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014, 1(1): 402-412.

[12] Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016, 1: 1105-1116.

[13] Zheng S, Wang F, Bao H, et al. Joint extraction of entities and relations based on a novel tagging scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2014, 1: 1227-1236.

[14] Wei X, Su J. Tplinker: Single-stage joint extraction of entities and relations through token pair linking. arXiv preprint arXiv:2010. 13415, 2020.

[15] Zeng X, Zeng D, He S, et al. Extracting relational facts by an end-to-end neural model with copy mechanism. Proceedings of the 27th International Conference on Computational Linguistics, 2018: 5068-5078.

[16] Han X, Yu P, Liu Z, et al. Graphrel: Modeling text as relational graphs for joint entity and relation extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 1409-1418.

[17] Xu Y, Feng Y, Huang S, et al. Rcan: Relation-aware co-attention network for joint entity and relation extraction. Knowledge-Based Systems, 2020, 199: 105912.

[18] Qu Y, Chen X, Ren X. Atlop: Adaptive thresholding for long-tailed relation extraction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020: 5284-5293.

[19] Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, 1: 4171-4186.

[20] Soares L B, FitzGerald N, Ling J, et al. Matching the blanks: Distributional similarity for relation learning. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 2895-2905.

[21] Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation, 1997, 9(8): 1735-1780.

[22] Zhang Y, Zhong V, Chen D. Position-aware attention and supervised data improve slot filling. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, 1: 221-231.

[23] Guo Z, Zhang Y, Lu W, et al. Knowledge-aware graph networks with label smoothness regularization for relation extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 1920-1930.

[24] Wang S, Han X, Zhao J. A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering, 2021.

[25] Vaswani A. Attention is all you need. Advances in neural information processing systems, 2017, 30.

[26] Gardent C, Shimorina A, Narayan S, et al. Webnlg challenge: Generating text from rdf data. Proceedings of the 10th International Conference on Natural Language Generation, 2017: 124-133.