A Survey of Information Extraction Based on Deep Learning
Source abstract
Abstract
As a core task and an important link in the fields of natural language understanding and information retrieval, information extraction (IE) can structure and semanticize unstructured multi-modal information. In recent years, deep learning (DL) has attracted considerable research attention to IE tasks. Deep learning-based entity relation extraction techniques have gradually surpassed traditional feature- and kernel-function-based methods in terms of the depth of feature extraction and model accuracy. In this paper, we explain the basic concepts of IE and DL, primarily expounding on the research progress and achievements of DL technologies in the field of IE. At the level of IE tasks, it is expounded from entity relationship extraction, event extraction, and multi-modal information extraction three aspects, and creates a comparative analysis of various extraction techniques. We also summarize the prospects and development trends in DL in the field of IE as well as difficulties requiring further study. It is believed that research can be carried out in the direction of multi-model and multi-task joint extraction, information extraction based on knowledge enhancement, and information fusion based on multi-modal at the method level. At the model level, further research should be carried out in the aspects of strengthening theoretical research, model lightweight, and improving model generalization ability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Natural Language Processing Techniques · Web Data Mining and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Funding
National Key R&D Program of China · Key Project of Beijing Social Science Fund · Social Sciences of the Basic Scientific Research of the Central Universities · Basic Scientific Research of the Central Universities for Postgraduates
References (100+)
Show source-provided references
1. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
2. Deep learning10.1038/nature14539 ↗
3. Named entity recognition based on deep learning
4. Xu, Y.L., Li, W.F., and Zhou, C.J. (2018, January 24–28). A survey of deep learning based natural language processing. Proceedings of the 22nd Annual Conference on New Network Technologies and Applications of Network Application Branch of China Computer Users Association in 2018, SuZhou, China.
5. A survey of natural language processing based on Deep learning
6. Research Progress of Natural Language Processing Based on Deep Learning
7. Survey of multimodal deep learning
8. A survey on deep learning techniques for image and video semantic segmentation10.1016/j.asoc.2018.05.018 ↗
9. Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge10.1186/s12911-021-01733-1 ↗
10. Software Knowledge Entity Relation Extraction with Entity-Aware and Syntactic Dependency Structure Information
11. Chinese Relation Extraction on Forestry Knowledge Graph Construction10.32604/csse.2021.014448 ↗
12. Review of Entity Relation Extraction Methods
13. Zeng, D.J., Liu, K., Lai, S.W., Zhou, G.Y., and Zhao, J. (2014, January 23–29). Relation Classification via Convolutional Deep Neural Network. Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics, Dublin, Ireland.
14. Liu, C., Sun, W., Chao, W., and Che, W. (2013). Convolution Neural Network for Relation Extraction. International Conference on Advanced Data Mining and Applications, Springer.10.1007/978-3-642-53917-6_21 ↗
15. Lavin, A., and Gray, S. (2016, January 27–30). Fast Algorithms for Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR.2016.435 ↗
16. Recent advances in convolutional neural networks10.1016/j.patcog.2017.10.013 ↗
17. Gu, J., Wang, G., Cai, J., and Chen, T. (2017, January 22–29). An Empirical Study of Language CNN for Image Captioning. Proceedings of the 16th IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.138 ↗
18. Chemical-induced disease relation extraction with dependency information and prior knowledge10.1016/j.jbi.2018.07.007 ↗
19. Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts10.1016/j.eswa.2018.08.009 ↗
20. Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings10.1155/2019/6789520 ↗
21. A relationship extraction method for domain knowledge graph construction10.1007/s11280-019-00765-y ↗
22. Image Segmentation Using Deep Learning: A Survey
23. Recurrent neural networks with segment attention and entity description for relation extraction from clinical texts10.1016/j.artmed.2019.04.003 ↗
24. Lin, C., Miller, T., Dligach, D., Amiri, H., Bethard, S., and Savova, G. (November, January 31). Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction. Proceedings of the 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, Brussels, Belgium.10.18653/v1/W18-5619 ↗
25. Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism10.1109/ACCESS.2020.2995739 ↗
26. Kim, K., Hur, Y., Kim, G., and Lim, H. (2020). GREG: A Global Level Relation Extraction with Knowledge Graph Embedding. Appl. Sci., 10.10.3390/app10031181 ↗
27. Bianchini, M., Dimitri, G.M., Maggini, M., and Scarselli, F. (2018). Deep neural networks for structured data. Computational Intelligence for Pattern Recognition, Springer.10.1007/978-3-319-89629-8_2 ↗
28. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M. (2018). Graph neural networks: A review of methods and applications. arXiv.
29. Document-Level Relation Extraction Based on Graph Attention Convolutional Neural Network
30. AGCN: Attention-based graph convolutional networks for drug-drug interaction extraction10.1016/j.eswa.2020.113538 ↗
31. Xu, J., Chen, Y., Qin, Y., Huang, R., and Zheng, Q. (2021). A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction. Symmetry, 13.10.3390/sym13081458 ↗
32. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
33. Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation10.1016/j.aej.2020.10.046 ↗
34. Zhang, H.Y., Huang, Z., Li, Z., Li, D., and Liu, F. (2021, January 11–14). Densely Connected Graph Attention Network Based on Iterative Path Reasoning for Document-Level Relation Extraction. Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Online.10.1007/978-3-030-75765-6_22 ↗
35. Extracting Biomedical Entity Relations using Biological Interaction Knowledge10.1007/s12539-021-00425-8 ↗
36. A hybrid model based on neural networks for biomedical relation extraction10.1016/j.jbi.2018.03.011 ↗
37. Extracting chemical–protein relations with ensembles of SVM and deep learning models10.1093/database/bay073 ↗
38. Chemical-protein interaction extraction via Gaussian probability distribution and external biomedical knowledge10.1093/bioinformatics/btaa491 ↗
39. Large-scale relation extraction from web documents and knowledge graphs with human-in-the-loop10.1016/j.websem.2019.100546 ↗
40. N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation10.3233/SW-170269 ↗
41. Knowledge-Enhanced Relation Extraction for Chinese EMRs10.1109/MITP.2020.2984598 ↗
42. The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts10.1093/jamia/ocaa205 ↗
43. Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction10.1109/TCBB.2018.2838661 ↗
44. Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction10.1186/s12859-020-03629-9 ↗
45. Zhou, W., Huang, K., Ma, T., and Huang, J. (2021, January 2–9). Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. Proceedings of the 35th AAAI Conference on Artificial Intelligence/33rd Conference on Innovative Applications of Artificial Intelligence/11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, Online.10.1609/aaai.v35i16.17717 ↗
46. Yang, S., Yoo, S., and Jeong, O. (2020). DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT. Appl. Sci., 10.10.3390/app10186429 ↗
47. KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions10.1016/j.jbi.2021.103834 ↗
48. Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., and Kaiser, Ł. (2018). Universal transformers. arXiv.
49. Hao, J., Wang, X., Yang, B., Wang, L., Zhang, J., and Tu, Z. (2019, January 2–7). Modeling recurrence for transformer. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA.10.18653/v1/N19-1122 ↗
50. Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., and Dean, J. (2018). Efficient neural architecture search via parameter sharing. arXiv.
51. Joint entity and relation extraction based on a hybrid neural network10.1016/j.neucom.2016.12.075 ↗
52. Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., and Xu, B. (August, January 30). Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. Proceedings of the 55th Annual Meeting of the Association-for-Computational-Linguistics (ACL), Vancouver, BC, Canada.
53. A Deep Neural Network Model for Joint Entity and Relation Extraction10.1109/ACCESS.2019.2949086 ↗
54. A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction
55. Knowledge graph construction based on knowledge enhanced word embedding model in manufacturing domain10.3233/JIFS-210982 ↗
56. Yadav, S., Lokala, U., Daniulaityte, R., Thirunarayan, K., Lamy, F., and Sheth, A. (2021). When they say weed causes depression, but it’s your fav antidepressant: Knowledge-aware attention framework for relationship extraction. PLoS ONE, 16.10.1371/journal.pone.0248299 ↗
57. Fu, T.-J., Li, P.-H., and Ma, W.-Y. (August, January 28). GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction. Proceedings of the 57th Annual Meeting of the Association-for-Computational-Linguistics (ACL), Florence, Italy.
58. Joint entity relation extraction based on graph neural network
59. Qiao, Y.P., Yu, Y.X., Liu, S.Y., Wang, Z.T., Xia, Z.F., and Qiao, J.Q. (2022). Graph Convolution-Enhanced Joint Entity and Relation Extraction Model by Multi-Channel Decoding. J. Comput. Res. Dev., 1–14.
60. Improving Distantly Supervised Relation Extraction by Knowledge Base-Driven Zero Subject Resolution10.1587/transinf.2017EDL8270 ↗
61. Zeng, D., Liu, K., Chen, Y., and Zhao, J. (2015, January 17–21). Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. Proceedings of the EMNLP, Lisbon, Portugal.10.18653/v1/D15-1203 ↗
62. Knowledge-embodied attention for distantly supervised relation extraction10.3233/IDA-194476 ↗
63. Graph-based reasoning model for multiple relation extraction10.1016/j.neucom.2020.09.025 ↗
64. Distantly supervised Web relation extraction for knowledge base population10.3233/SW-150180 ↗
65. Long-distance disorder-disorder relation extraction with bootstrapped noisy data10.1016/j.jbi.2020.103529 ↗
66. Local-to-global GCN with knowledge-aware representation for distantly supervised relation extraction10.1016/j.knosys.2021.107565 ↗
67. Distant Supervision Relation Extraction via adaptive dependency-path and additional knowledge graph supervision10.1016/j.neunet.2020.10.012 ↗
68. Mao, N., Huang, W., and Zhong, H. (2021). KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction. Appl. Sci., 11.10.3390/app11167734 ↗
69. Wang, Z., Wen, R., Chen, X., Huang, S., Zhang, N., and Zheng, Y. (2022, January 12–17). Finding Influential Instances for Distantly Supervised Relation Extraction. Proceedings of the COLING, Gyeongju, Korea.
70. A Joint Extraction Method of Financial Events Based on Multi-Layer Convolutional Neural Networks
71. Chen, Y., Xu, L., Liu, K., Zeng, D., and Zhao, J. (2015, January 2–22). Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks. Proceedings of the ACL, Austin, TX, USA.10.3115/v1/P15-1017 ↗
72. Liu, S., Chen, Y., Liu, K., and Zhao, J. (August, January 30). Exploiting argument information to improve event detection via supervised attention mechanisms. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, Vancouver, BC, USA.
73. Nguyen, T.H., Cho, K., and Grishman, R. (2016, January 12–17). Joint Event Extraction via Recurrent Neural Networks. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.10.18653/v1/N16-1034 ↗
74. Joint Extraction Model for Entities and Events with Multi-task Deep Learning
75. Kruengkrai, C., Nguyen, T.H., Aljunied, S.M., and Bing, L. (2020, January 5–10). Improving LowResource Named Entity Recognition Using Joint Sentence and Token Labeling. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.10.18653/v1/2020.acl-main.523 ↗
76. Martins, P.H., Marinho, Z., and Martins, A.F.T. (August, January 28). Joint Learning of Named Entity Recognition and Entity Linking. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Florence, Italy.
77. Joint Chinese Event Extraction Based Multi-Task Learning
78. Lin, Y., Yang, S.Q., Stoyanov, V., and Ji, H. (2018, January 15–20). A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia.10.18653/v1/P18-1074 ↗
79. Wang, J., Kulkarni, M., and Preotiuc-Pietro, D. (2020, January 5–10). Multi-domain Named Entity Recognition with Genre-aware and Agnostic Inference. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.10.18653/v1/2020.acl-main.750 ↗
80. Lu, Y., Liu, Q., Dai, D., Xiao, X., Lin, H., Han, X., Sun, L., and Wu, H. (2022). Unified Structure Generation for Universal Information Extraction. arXiv.10.18653/v1/2022.acl-long.395 ↗
81. Sentence-level Joint Event Extraction of Traditional Chinese Medical Literature
82. Extracting Events from Ancient Books Based on RoBERTaCRF
83. Hsu, I.-H., Huang, K.-H., Boschee, E., Miller, S., Natarajan, P., Chang, K.-W., and Peng, N. (2022, January 10–15). DEGREE: A Data-Efficient Generation-Based Event Extraction Model. Proceedings of the NAACL, Seattle, WA, USA.10.18653/v1/2022.naacl-main.138 ↗
84. Feng, X., Qin, B., and Liu, T.A. (2016, January 7–12). Language-Independent Neural Network for Event Detection. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.10.18653/v1/P16-2011 ↗
85. Miao, J., Duan, Y., Zhang, Y., and Zhang, Z. (2021). Method for extracting event trigger words based on the CNNBiGRU model. Comput. Eng., 47.
86. Joint Extraction of Entities and Events by Hybrid Neural Network
87. Yang, H., Chen, Y.B., Liu, K., Xiao, Y., and Zhao, J. (2018, January 22–27). DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics-System Demonstrations, Dublin, Ireland.10.18653/v1/P18-4009 ↗
88. Document-level Event Extraction Based on Joint Labeling and Global Reasoning
89. Zheng, S., Cao, W., Xu, W., and Bian, J. (2019, January 3–7). Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China.10.18653/v1/D19-1032 ↗
90. Guo, X., Gao, C.X., Chen, Q., Wang, S.G., and Wang, X.J. (2021). Three-stage Document-level Event Extraction for COVID-19 News. Comput. Eng. Appl., 1–12.
91. Yang, B.S., and Mitchell, T.M. (2016). Joint Extraction of Events and Entities within a Document Context. arXiv.10.18653/v1/N16-1033 ↗
92. Liu, J., Chen, Y., Liu, K., Bi, W., and Liu, X. (2020, January 16–20). Event Extraction as Machine Reading Comprehension. Proceedings of the Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online.10.18653/v1/2020.emnlp-main.128 ↗
93. Liu, Z.Y., Yu, W.H., Hong, Z.Y., Ke, G.Z., and Tan, R.J. (2022). Chinese Event Extraction Using Question Answering. Comput. Eng. Appl., 1–8.
94. Survey on construction and application research for multi-modal knowledge graphs
95. Liu, X., Gao, F., Zhang, Q., and Zhao, H. (2019, January 2–7). Graph Convolution for Multimodal Information Extraction from Visually Rich Documents. Proceedings of the NAACL, Minneapolis, MN, USA.10.18653/v1/N19-2005 ↗
96. Wang, M., Wang, H.F., Li, B.H., Zhao, X., and Wang, X. (2022). Survey of Key Technologies of New Generation Knowledge Graph. J. Comput. Res. Dev., 1–18.
97. Sun, L., Wang, J.Q., Zhang, K., Su, Y.D., and Weng, F.S. (2021, January 2–9). RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER. Proceedings of the 35th AAAI Conference on Artificial Intelligence/33rd Conference on Innovative Applications of Artificial Intelligence/11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, Online.10.1609/aaai.v35i15.17633 ↗
98. Zhang, Q., Fu, J., Liu, X., and Huang, X. (2018, January 2–7). Adaptive Co-Attention Network for Named Entity Recognition in Tweets. Proceedings of the 32nd AAAI Conference on Artificial Intelligence/30th Innovative Applications of Artificial Intelligence Conference/8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.11962 ↗
99. Zhang, D., Wei, S., Li, S., Wu, H., Zhu, Q., and Zhou, G. (2021, January 2–9). Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance. Proceedings of the 35th AAAI Conference on Artificial Intelligence/33rd Conference on Innovative Applications of Artificial Intelligence/11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, Online.10.1609/aaai.v35i16.17687 ↗
100. Sui, D., Tian, Z., Chen, Y., Liu, K., and Zhao, Y. (2021, January 1–6). A Large-Scale Chinese Multimodal NER Dataset with Speech Clues. Proceedings of the Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL)/11th International Joint Conference on Natural Language Processing (IJCNLP)/6th Workshop on Representation Learning for NLP (RepL4NLP), Electr Network, Online.10.18653/v1/2021.acl-long.218 ↗