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Joint extraction of entities and relations using multi-label tagging and relational alignment

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Abstract

Relation extraction aims to identify semantic relations between entities in text. In recent years, this task has been extended to the joint extraction of entities and relations, which requires the simultaneous identification of entities and their relations from sentences. However, existing methods, limited by the existing tagging scheme, fail to identify more complex entities, which in turn limits the performance of the joint extraction task. This article presents a joint extraction model for entities and relations called MLRA-LSTM-CRF that uses multi-label tagging and relational alignment to transform this task into a multi-label tag recognition problem. The proposed model first tags the entities and their relations according to the multi-label tagging scheme and then uses a joint entity and relation extraction module with a multi-layer attention mechanism to extract the triplets in the sentence. Finally, the relational alignment module is used to align the predicted relation classification results. Experimental results on the New York Times and Wiki-KBP datasets indicate that MLRA-LSTM-CRF is significantly better than that of several state-of-the-art models and baseline.

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Notes

  1. https://github.com/shanzhenren/CoType.

  2. https://github.com/LiyuanLucasLiu/ReHession.

References

  1. Golshan PN, Dashti HR, Azizi S, Safari L (2018) A study of recent contributions on information extraction. arXiv preprint http://arxiv.org/abs/180305667

  2. Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Lingvist Investig 30(1):3–26. https://doi.org/10.1162/COLI_a_00178

    Article  Google Scholar 

  3. Fei H, Ren Y, Ji D (2020) Dispatched attention with multi-task learning for nested mention recognition. Inf Sci 513:241–251. https://doi.org/10.1016/j.ins.2019.10.065

    Article  Google Scholar 

  4. Rink B, Harabagiu S (2010) Utd: Classifying semantic relations by combining lexical and semantic resources. In: Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval), pp 256–259, https://www.aclweb.org/anthology/S10-1057

  5. Wang H, Qin K, Lu G, Luo G, Liu G (2020) Direction-sensitive relation extraction using bi-sdp attention model. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2020.105928

    Article  Google Scholar 

  6. Li Q, Ji H (2014) Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp 402–412, https://www.aclweb.org/anthology/P14-1038

  7. Singh S, Riedel S, Martin B, Zheng J, McCallum A (2013) Joint inference of entities, relations, and coreference. In: Proceedings of the 2013 Workshop on Automated Knowledge Base Construction (AKBC), pp 1–6, https://doi.org/10.1145/2509558.2509559

  8. Miwa M, Sasaki Y (2014) Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1858–1869, https://www.aclweb.org/anthology/D14-1200

  9. Ren X, Wu Z, He W, Qu M, Voss CR, Ji H, Abdelzaher TF, Han J (2017) Cotype: Joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web (WWW), pp 1015–1024, https://doi.org/10.1145/3038912.3052708

  10. Miwa M, Bansal M (2016) End-to-end relation extraction using lstms on sequences and tree structures. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1105–1116, https://www.aclweb.org/anthology/P16-1105

  11. Zheng S, Xu J, Zhou P, Bao H, Qi Z, Xu B (2016) A neural network framework for relation extraction: learning entity semantic and relation pattern. Knowl Based Syst 114:12–23. https://doi.org/10.1016/j.knosys.2016.09.019

    Article  Google Scholar 

  12. Zheng S, Hao Y, Lu D, Bao H, Xu J, Hao H, Xu B (2017) Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257:59–66. https://doi.org/10.1016/j.neucom.2016.12.075

    Article  Google Scholar 

  13. Wang S, Zhang Y, Che W, Liu T (2018) Joint extraction of entities and relations based on a novel graph scheme. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp 4461–4467, https://doi.org/10.24963/ijcai.2018/620

  14. Lei M, Huang H, Feng C, Gao Y, Su C (2019) An input information enhanced model for relation extraction. Neural Comput Appl 31(12):9113–9126. https://doi.org/10.1007/s00521-019-04430-3

    Article  Google Scholar 

  15. Dai D, Xiao X, Lyu Y, Dou S, She Q, Wang H (2019) Joint extraction of entities and overlapping relations using position-attentive sequence labeling. Proc AAAI Conf Artif Intell (AAAI) 33:6300–6308. https://doi.org/10.1609/aaai.v33i01.33016300

    Article  Google Scholar 

  16. Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B (2017) Joint extraction of entities and relations based on a novel tagging scheme. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1227–1236, https://doi.org/10.18653/v1/P17-1113

  17. Ratinov L, Roth D (2009) Design challenges and misconceptions in named entity recognition. In: Proceedings of the 13th Conference on Computational Natural Language Learning (CoNLL), pp 147–155, https://www.aclweb.org/anthology/W09-1119

  18. Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 103–111, https://doi.org/10.3115/v1/W14-4012

  19. Passos A, Kumar V, McCallum A (2014) Lexicon infused phrase embeddings for named entity resolution. In: Proceedings of the 18th Conference on Computational Natural Language Learning (CoNLL), pp 78–86, https://www.aclweb.org/anthology/W14-1609

  20. Luo G, Huang X, Lin CY, Nie Z (2015) Joint entity recognition and disambiguation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 879–888, https://www.aclweb.org/anthology/D15-1104

  21. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537, https://dl.acm.org/citation.cfm?id=2078186

  22. Rei M, Crichton GK, Pyysalo S (2016) Attending to characters in neural sequence labeling models. In: Proceedings of 26th International Conference on Computational Linguistics (COLING), pp 309–318, https://www.aclweb.org/anthology/C16-1030

  23. Kambhatla N (2004) Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions (ACL), pp 22–25, https://www.aclweb.org/anthology/P04-3022

  24. Socher R, Huval B, Manning CD, Ng AY (2012) Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp 1201–1211, https://www.aclweb.org/anthology/D12-1110/

  25. Xu Y, Mou L, Li G, Chen Y, Peng H, Jin Z (2015) Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1785–1794, https://www.aclweb.org/anthology/D15-1206

  26. Zhang S, Zheng D, Hu X, Yang M (2015) Bidirectional long short-term memory networks for relation classification. In: Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation (PACLIC), pp 73–78, https://www.aclweb.org/anthology/Y15-1009/

  27. Xu Y, Jia R, Mou L, Li G, Chen Y, Lu Y, Jin Z (2016) Improved relation classification by deep recurrent neural networks with data augmentation. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING), pp 1461–1470, https://www.aclweb.org/anthology/C16-1138/

  28. Xu K, Feng Y, Huang S, Zhao D (2015) Semantic relation classification via convolutional neural networks with simple negative sampling. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), https://www.aclweb.org/anthology/D15-1062

  29. dos Santos C, Xiang B, Zhou B (2015) Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL), pp 626–634, https://www.aclweb.org/anthology/P15-1061

  30. Wang L, Cao Z, De Melo G, Liu Z (2016) Relation classification via multi-level attention cnns. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1298–1307, https://www.aclweb.org/anthology/P16-1123

  31. Vu NT, Adel H, Gupta P, Schütze H (2016) Combining recurrent and convolutional neural networks for relation classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (ACL), pp 534–539, https://www.aclweb.org/anthology/N16-1065

  32. Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp 207–212, https://www.aclweb.org/anthology/P16-2034

  33. Zheng S, Xu J, Bao H, Qi Z, Zhang J, Hao H, Xu B (2016) Joint learning of entity semantics and relation pattern for relation extraction. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pp 443–458, https://doi.org/10.1007/978-3-319-46128-1_28

  34. Katiyar A, Cardie C (2017) Going out on a limb: Joint extraction of entity mentions and relations without dependency trees. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp 917–928, https://www.aclweb.org/anthology/P17-1085

  35. Tan Z, Zhao X, Wang W, Xiao W (2019) Jointly extracting multiple triplets with multilayer translation constraints. Proc AAAI Conf Artif Intell (AAAI) 33:7080–7087. https://doi.org/10.1609/aaai.v33i01.33017080

    Article  Google Scholar 

  36. Fei H, Ren Y, Ji D (2020) Boundaries and edges rethinking: an end-to-end neural model for overlapping entity relation extraction. Inf Process Manag 57(6). https://doi.org/10.1016/j.ipm.2020.102311

    Article  Google Scholar 

  37. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (NIPS), pp 3111–3119, http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-theircompositionality

  38. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems (NIPS), pp 5998–6008, http://papers.nips.cc/paper/7181-attention-is-all-you-need

  39. Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 720–728, https://www.aclweb.org/anthology/D14-1080

  40. Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML), pp 282–289

  41. Pinto D, McCallum A, Wei X, Croft WB (2003) Table extraction using conditional random fields. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp 235–242, https://doi.org/10.1145/860435.860479

  42. Xue N, Shen L (2003) Chinese word segmentation as lmr tagging. In: Proceedings of the Second Workshop on Chinese Language Processing (SIGHAN), pp 176–179, https://doi.org/10.3115/1119250.1119278

  43. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), pp 1–15, arXiv:abs/1409.0473

  44. Ji Y, Zhang H, Jie Z, Ma L, Wu QJ (2020) Casnet: a cross-attention siamese network for video salient object detection. IEEE Trans Neural Networks Learn Syst pp 1–15

  45. He S, Liu K, Ji G, Zhao J (2015) Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), pp 623–632, https://doi.org/10.1145/2806416.2806502

  46. Hoffmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL), pp 541–550, https://www.aclweb.org/anthology/P11-1055

  47. Ellis J, Li X, Griffitt K, Strassel SM, Wright J (2013) Linguistic resources for 2013 knowledge base population evaluations. In: Proceedings of the Sixth Text Analysis Conference (TAC), https://tac.nist.gov/publications/2013/additional.papers/KBP2013_annotation_overview.TAC2013.proceedings.pdf

  48. Liu L, Ren X, Zhu Q, Zhi S, Gui H, Ji H, Han J (2017) Heterogeneous supervision for relation extraction: A representation learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 46–56, https://www.aclweb.org/anthology/D17-1005

  49. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (ACL), pp 260–270, https://www.aclweb.org/anthology/N16-1030

  50. Mukkamala MC, Hein M (2017) Variants of rmsprop and adagrad with logarithmic regret bounds. In: Proceedings of the 34th International Conference on Machine Learning (ICML), pp 2545–2553, http://proceedings.mlr.press/v70/mukkamala17a.html

  51. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958, http://dl.acm.org/citation.cfm?id=2670313

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Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant 2021YFB3900601, the Graduate Research and Innovation Projects of Jiangsu Province under Grant KYCX19\_0507, the Fundamental Research Funds for the Central Universities under Grant 2019B64214, and the University Natural Science Research Projects of Anhui Province under Grant KJ2019A1277.

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Authors and Affiliations

  1. Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China

    Tingting Hang, Jun Feng, Le Yan, Yunfeng Wang & Jiamin Lu

  2. School of Computer and Information, Hohai University, Nanjing, China

    Tingting Hang, Jun Feng, Le Yan, Yunfeng Wang & Jiamin Lu

  3. College of Electricity and Information, Wanjiang University of Technology, Maanshan, China

    Tingting Hang

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  1. Tingting Hang
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Hang, T., Feng, J., Yan, L. et al. Joint extraction of entities and relations using multi-label tagging and relational alignment. Neural Comput & Applic 34, 6397–6412 (2022). https://doi.org/10.1007/s00521-021-06685-1

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