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| Automated Knowledge Graph Construction for Judicial Case Facts |
| HONG Wenxing1, HU Zhiqiang1, WENG Yang2, ZHANG Heng3, WANG Zhu4, GUO Zhixin5 |
1.School of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361102, China;
2.School of Mathematics, Sichuan University, Chengdu, Sichuan 610065, China;
3.Galawxy Inc., Chengdu, Sichuan 610036, China;
4.School of Law, Sichuan University, Chengdu, Sichuan 610207, China;
5.School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China |
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Abstract Legal knowledge centered cognitive intelligence is an important topic for judicial artificial intelligence. This paper proposes an automated knowledge graph construction approach for judicial case facts. Based on the pre-training model, models for entity recognition and relation extraction are presented. For the entity recognition task, two pre-training based entity recognition models are compared. For the relation extraction task, a multi-task joint semantic relation extraction model is proposed incorporating translating embeddings. The knowledge representation learning of case facts is obtained while completing the relation extraction task. For “motor vehicle traffic accident liability dispute”, compared with the baseline model, the entity recognition can be increased by 0.36 in F1 score, and the relation extraction by 2.37 F1 score. Based on the proposed method, a case facts knowledge graphs are established on a couple of hundred thousand judicial documents, enabling the semantic computing for judicial artificial intelligence applications such as case retrieval.
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Received: 16 September 2019
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[1]Bollacker K, Evans C, Paritosh P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, 2008: 1247-1250.
[2]Vrandeic D, Krtzsch M. Wikidata: a free collaborative knowledge base[J]. Communications of the ACM, 2014, 57(10): 78-85.
[3]Bizer C, Lehmann J, Kobilarov G, et al. DBpedia -a crystallization point for the web of data [J]. Journal of Web Semantics, 2009, 7(3): 154-165.
[4]Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge[C]//Proceedings of the 16th International Conference on World Wide Web, 2007: 697-706.
[5]Niu X, Sun X, Wang H, et al. Zhishi.me -weaving Chinese linking open data[C]//Proceedings of the 10th International Semantic Web Conference, 2011: 205-220.
[6]Xu B, Xu Y, Liang J, et al. CN-DBpedia: a never-ending Chinese knowledge extraction system[C]//Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2017: 428-438.
[7]Tang J, Zhang J, Yao L, et al. ArnetMiner: extraction and mining of academic social networks[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008: 990-998.
[8]Wang R, Yan Y, Wang J, et al. AceKG: a large-scale knowledge graph for academic data mining[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018: 1487-1490.
[9]Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J/OL]. arXiv preprint arXiv: 1508.01991, 2015.
[10]Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016: 260-270.
[11]Strubell E, Verga P, Belanger D, et al. Fast and accurate entity recognition with iterated dilated convolutions[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017: 2670-2680.
[12]Ma X, Hovy E. End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 1064-1074.
[13]Chiu J P C, Nichols E. Named entity recognition with bidirectional LSTM-CNNs[J]. Transactions of the Association for Computational Linguistics, 2016, 4: 357-370.
[14]Zhang S, Zheng D, Hu X, et al. Bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 29th Pacific Asia Conference on Language, 2015: 73-78.
[15]Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 207-212.
[16]Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network[C]//Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers, 2014: 2335-2344.
[17]Shen Y, Huang X. Attention-based convolutional neural network for semantic relation extraction[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, 2016: 2526-2536.
[18]Wang L, Cao Z, De Melo G, et al. Relation classification via multi-level attention CNNs[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 1298-1307.
[19]Zhang X, Chen F, Huang R. A combination of RNN and CNN for attention-based relation classification[J]. Procedia Computer Science, 2018, 131: 911-917.
[20]Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems, 2013: 3111-3119.
[21]Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[22]Peters M E, Neumann M, Iyyer M, et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 2227-2237.
[23]Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 5998-6008.
[24]Radford A, Narasimhan K, Salimans T, et al. Improving language understanding by generative pre-training[J/OL]. 2018, https: //s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.
[25]Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv: 1810.04805, 2018.
[26]Alt C, Hübner M, Hennig L. Improving relation extraction by pre-trained language representations[C]//Proceedings of the 2019 Conference on Automated Knowledge Base Construction, 2019.
[27]He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[28]Ba J L, Kiros J R, Hinton G E. Layer normalization[J]. arXiv preprint arXiv: 1607.06450, 2016.
[29]Lafferty J, McCallum A, Pereira F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning, 2001: 282-289.
[30]Forney G D. The viterbi algorithm[C]//Proceedings of the IEEE, 1973, 61(3): 268-278.
[31]Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems, 2013: 2787-2795.
[32]Stenetorp P, Pyysalo S, Topic G, et al. BRAT: a web-based tool for NLP-assisted text annotation[C]//Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics, 2012: 102-107.
[33]Micikevicius P, Narang S, Alben J, et al. Mixed precision training[C]//Proceedings of International Conference on Learning Representations, 2017.
[34]Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
[35]Hendrickx I, Kim S N, Kozareva Z, et al. Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals[C]//Proceedings of the 5th International Workshop on Semantic Evaluation, 2010: 33-38. |
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2020, Vol. 34 
