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Entity and relation extraction with rule-guided dictionary as domain knowledge |
Xinzhi WANG1( ), Jiahao LI1, Ze ZHENG2, Yudong CHANG1, Min ZHU3 |
1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2. Baidu (China) Co., Ltd., Beijing 100085, China
3. The Sixth Medical Center of PLA General Hospital, Beijing 100048, China |
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Abstract Entity and relation extraction is an indispensable part of domain knowledge graph construction, which can serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis. The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets. However, the performance of these methods degrades when they face domain-specific datasets. Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains. Relation extraction is based on the hypothesis that the relations hidden in sentences are unified, thereby neglecting that relations may be diverse in different entity tuples. To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’ dependence. Second, domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction. Finally, experiments were designed to verify the effectiveness of our proposed methods. Experimental results on two domains, including laser industry and unmanned ship, showed the superiority of our methods. The F1 value on laser industry entity, unmanned ship entity, laser industry relation, and unmanned ship relation datasets is improved by +1%, +6%, +2%, and +1%, respectively. In addition, the extraction accuracy of entity relation triplet reaches 83% and 76% on laser industry entity pair and unmanned ship entity pair datasets, respectively.
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| Keywords
entity extraction
relation extraction
prior knowledge
domain rule
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Corresponding Author(s):
Xinzhi WANG
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Just Accepted Date: 14 September 2022
Online First Date: 31 October 2022
Issue Date: 08 December 2022
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| 1 |
R C Bunescu, R J Mooney, (2005). A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Vancouver: Association for Computational Linguistics, 724–731
|
| 2 |
M Eberts, A Ulges, (2021). An end-to-end model for entity-level relation extraction using multi-instance learning. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 3650–3660
|
| 3 |
Z Geng, G Chen, Y Han, G Lu, F Li, (2020). Semantic relation extraction using sequential and tree-structured LSTM with attention. Information Sciences, 509: 183–192
https://doi.org/10.1016/j.ins.2019.09.006
|
| 4 |
R (1995) Grishman. The NYU system for MUC-6 or where’s the syntax? In: Proceedings of the 6th Conference on Message Understanding. Columbia, MD: Association for Computational Linguistics, 167–175
|
| 5 |
M A Hearst, (1992). Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics. Nantes: Association for Computational Linguistics, 539–545
|
| 6 |
K HumphreysR GaizauskasS AzzamC HuyckB MitchellH CunninghamY Wilks (1998). University of Sheffield: Description of the LaSIE-II system as used for MUC-7. In: Proceedings of the 7th Message Understanding Conference. Fairfax, VA, M98-1007
|
| 7 |
H Isozaki, H Kazawa, (2002). Efficient support vector classifiers for named entity recognition. In: Proceedings of the 19th International Conference on Computational Linguistics. Taipei: Association for Computational Linguistics, 1–7
|
| 8 |
S B Kim, K S Han, H C Rim, S H Myaeng, (2006). Some effective techniques for Naive Bayes text classification. IEEE Transactions on Knowledge and Data Engineering, 18( 11): 1457–1466
https://doi.org/10.1109/TKDE.2006.180
|
| 9 |
D P KingmaJ Ba (2014). Adam: A method for stochastic optimization. arXiv preprint. arXiv:1412.6980
|
| 10 |
J D LaffertyA McCallumF C N (2001) Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers Inc., 282–289
|
| 11 |
Y LakretzG KruszewskiT DesbordesD HupkesS DehaeneM (2019) Baroni. The emergence of number and syntax units in LSTM language models. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, MN: Association for Computational Linguistics, 11–20
|
| 12 |
X Li, J Feng, Y Meng, Q Han, F Wu, J Li, (2020). A unified MRC framework for named entity recognition. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 5849–5859
|
| 13 |
Z Li, F Yang, Y Luo, (2019). Context embedding based on Bi-LSTM in semi-supervised biomedical word sense disambiguation. IEEE Access, 7: 72928–72935
https://doi.org/10.1109/ACCESS.2019.2912584
|
| 14 |
X D Lin, H Peng, B Liu, (2006). Chinese named entity recognition using support vector machines. In: International Conference on Machine Learning and Cybernetics. Dalian: IEEE, 4216–4220
|
| 15 |
P Lison, J Barnes, A Hubin, S Touileb, (2020). Named entity recognition without labelled data: A weak supervision approach. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1518–1533
https://doi.org/10.18653/v1/2020.acl-main.139
|
| 16 |
G Liu, J Guo, (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337: 325–338
https://doi.org/10.1016/j.neucom.2019.01.078
|
| 17 |
Y LuoF XiaoH (2020) Zhao. Hierarchical contextualized representation for named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. New York, NY: AAAI Press, 8441–8448
|
| 18 |
T MikolovI SutskeverK ChenG S CorradoJ Dean (2013). Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, NV: Curran Associates Inc., 3111–3119
|
| 19 |
G A Miller, (1995). WordNet: A lexical database for English. Communications of the ACM, 38( 11): 39–41
https://doi.org/10.1145/219717.219748
|
| 20 |
G Nan, Z Guo, I Sekulić, W Lu, (2020). Reasoning with latent structure refinement for document-level relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1546–1557
|
| 21 |
T NayakH T (2020) Ng. Effective modeling of encoder-decoder architecture for joint entity and relation extraction. In: Proceedings of the AAAI Conference on Artificial Intelligence. New York, NY: AAAI Press, 8528–8535
|
| 22 |
S Park, Y Kim, (2019). A method for sharing cell state for LSTM-based language model. In: International Conference on Intelligence Science: Computer and Information Science. Beijing: Springer, 81–94
https://doi.org/10.1007/978-3-030-25213-7_6
|
| 23 |
N Reimers, I Gurevych, (2017). Reporting score distributions makes a difference: Performance study of LSTM-networks for sequence tagging. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 338–348
https://doi.org/10.18653/v1/D17-1035
|
| 24 |
Y ShenJ Han (2020). Joint extraction of entity and relation with information redundancy elimination. arXiv preprint. arXiv:2011.13565
|
| 25 |
T Shibuya, E Hovy, (2020). Nested named entity recognition via second-best sequence learning and decoding. Transactions of the Association for Computational Linguistics, 8: 605–620
https://doi.org/10.1162/tacl_a_00334
|
| 26 |
A Thomas, S Sangeetha, (2020). Deep learning architectures for named entity recognition: A survey. In: Advanced Computing and Intelligent Engineering. Singapore: Springer, 215–225
|
| 27 |
A WaldisL Mazzola (2021). Nested and balanced entity recognition using multi-task learning. arXiv preprint. arXiv:2106.06216
|
| 28 |
T Wang, G Hirst, (2009). Extracting synonyms from dictionary definitions. In: Proceedings of the International Conference RANLP. Borovets: Association for Computational Linguistics, 471–477
|
| 29 |
X Wang, Y Chang, V Sugumaran, X Luo, P Wang, H Zhang, (2021a). Implicit emotion relationship mining based on optimal and majority synthesis from multimodal data prediction. IEEE MultiMedia, 28( 2): 96–105
https://doi.org/10.1109/MMUL.2021.3071495
|
| 30 |
X Wang, L Kou, V Sugumaran, X Luo, H Zhang, (2021b). Emotion correlation mining through deep learning models on natural language text. IEEE Transactions on Cybernetics, 51( 9): 4400–4413
https://doi.org/10.1109/TCYB.2020.2987064
|
| 31 |
H Yamada, T Kudo, Y Matsumoto, (2002). Japanese named entity extraction using support vector machine. Transactions of Information Processing Society of Japan, 43( 1): 44–53
|
| 32 |
Y Yao, L Rosasco, A Caponnetto, (2007). On early stopping in gradient descent learning. Constructive Approximation, 26( 2): 289–315
https://doi.org/10.1007/s00365-006-0663-2
|
| 33 |
G Zhou, J Su, J Zhang, M Zhang, (2005). Exploring various knowledge in relation extraction. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics. Ann Arbor, MI: Association for Computational Linguistics, 427–434
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