A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction

Siyao An; Tianhao Wang; Lirui Wang; Mingjun Zhong; Baili Zhang

doi:10.1007/978-3-031-06788-4_34

A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction

Siyao An, Tianhao Wang, Lirui Wang, Mingjun Zhong, Baili Zhang^* (Corresponding Author)

^*Corresponding author for this work

Southeast University, Nanjing

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

As a kind of long legal text with a fixed structure, the medical dispute judgment documents have a large amount of redundant information. The information directly harms the semantic representation of the documents and the recommendation of similar cases. Therefore, a semantic representation scheme for medical dispute judgment documents based on elements extraction is proposed in this paper. The scheme consists of two stages. In the first stage, key sentences and keywords are extracted from the original documents based on BERT+FC model and BERT+CRF model to filter the redundant information. In the second stage, text matching training and mask training based on specific keywords are carried out according to the extracted elements, and the BERT model is transferred to a semantic representation model through multi-task learning. The experiment results show that the semantic representation scheme can improve the accuracy of matching medical dispute judgment documents to 85.84%.

Original language	English
Title of host publication	Artificial Intelligence and Security
Subtitle of host publication	8th International Conference, ICAIS 2022, Proceedings
Editors	Xingming Sun, Xiaorui Zhang, Zhihua Xia, Elisa Bertino
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	400-414
Number of pages	15
ISBN (Electronic)	978-3-031-06788-4
ISBN (Print)	9783031067877
DOIs	https://doi.org/10.1007/978-3-031-06788-4_34
Publication status	Published - 4 Jul 2022
Event	8th International Conference on Artificial Intelligence and Security, ICAIS 2022 - Qinghai, China Duration: 15 Jul 2022 → 20 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13339 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Artificial Intelligence and Security, ICAIS 2022
Country/Territory	China
City	Qinghai
Period	15/07/22 → 20/07/22

Bibliographical note

Funding Information:
Acknowledgement. This work was partly supported by the National Key R&D Program of China (2018YFC0830200), the Fundamental Research Funds for the Central Universities (2242018S30021 and 2242017S30023) and Open Research Fund from Key Laboratory of Computer Network and Information Integration in Southeast University, Ministry of Education, China.

Keywords

Elements extraction
Medical dispute
Multi-task learning
Semantic representation

Access to Document

10.1007/978-3-031-06788-4_34Licence: Unspecified

Cite this

An, S., Wang, T., Wang, L., Zhong, M., & Zhang, B. (2022). A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction. In X. Sun, X. Zhang, Z. Xia, & E. Bertino (Eds.), Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Proceedings (pp. 400-414). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13339 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06788-4_34

A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction. / An, Siyao; Wang, Tianhao; Wang, Lirui et al.
Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Proceedings. ed. / Xingming Sun; Xiaorui Zhang; Zhihua Xia; Elisa Bertino. Springer Science and Business Media Deutschland GmbH, 2022. p. 400-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13339 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

An, S, Wang, T, Wang, L, Zhong, M & Zhang, B 2022, A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction. in X Sun, X Zhang, Z Xia & E Bertino (eds), Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13339 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 400-414, 8th International Conference on Artificial Intelligence and Security, ICAIS 2022, Qinghai, China, 15/07/22. https://doi.org/10.1007/978-3-031-06788-4_34

An S, Wang T, Wang L, Zhong M, Zhang B. A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction. In Sun X, Zhang X, Xia Z, Bertino E, editors, Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 400-414. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-06788-4_34

An, Siyao ; Wang, Tianhao ; Wang, Lirui et al. / A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction. Artificial Intelligence and Security : 8th International Conference, ICAIS 2022, Proceedings. editor / Xingming Sun ; Xiaorui Zhang ; Zhihua Xia ; Elisa Bertino. Springer Science and Business Media Deutschland GmbH, 2022. pp. 400-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "As a kind of long legal text with a fixed structure, the medical dispute judgment documents have a large amount of redundant information. The information directly harms the semantic representation of the documents and the recommendation of similar cases. Therefore, a semantic representation scheme for medical dispute judgment documents based on elements extraction is proposed in this paper. The scheme consists of two stages. In the first stage, key sentences and keywords are extracted from the original documents based on BERT+FC model and BERT+CRF model to filter the redundant information. In the second stage, text matching training and mask training based on specific keywords are carried out according to the extracted elements, and the BERT model is transferred to a semantic representation model through multi-task learning. The experiment results show that the semantic representation scheme can improve the accuracy of matching medical dispute judgment documents to 85.84%.",

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author = "Siyao An and Tianhao Wang and Lirui Wang and Mingjun Zhong and Baili Zhang",

note = "Funding Information: Acknowledgement. This work was partly supported by the National Key R&D Program of China (2018YFC0830200), the Fundamental Research Funds for the Central Universities (2242018S30021 and 2242017S30023) and Open Research Fund from Key Laboratory of Computer Network and Information Integration in Southeast University, Ministry of Education, China. ; 8th International Conference on Artificial Intelligence and Security, ICAIS 2022 ; Conference date: 15-07-2022 Through 20-07-2022",

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AU - Zhong, Mingjun

AU - Zhang, Baili

N1 - Funding Information: Acknowledgement. This work was partly supported by the National Key R&D Program of China (2018YFC0830200), the Fundamental Research Funds for the Central Universities (2242018S30021 and 2242017S30023) and Open Research Fund from Key Laboratory of Computer Network and Information Integration in Southeast University, Ministry of Education, China.

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N2 - As a kind of long legal text with a fixed structure, the medical dispute judgment documents have a large amount of redundant information. The information directly harms the semantic representation of the documents and the recommendation of similar cases. Therefore, a semantic representation scheme for medical dispute judgment documents based on elements extraction is proposed in this paper. The scheme consists of two stages. In the first stage, key sentences and keywords are extracted from the original documents based on BERT+FC model and BERT+CRF model to filter the redundant information. In the second stage, text matching training and mask training based on specific keywords are carried out according to the extracted elements, and the BERT model is transferred to a semantic representation model through multi-task learning. The experiment results show that the semantic representation scheme can improve the accuracy of matching medical dispute judgment documents to 85.84%.

AB - As a kind of long legal text with a fixed structure, the medical dispute judgment documents have a large amount of redundant information. The information directly harms the semantic representation of the documents and the recommendation of similar cases. Therefore, a semantic representation scheme for medical dispute judgment documents based on elements extraction is proposed in this paper. The scheme consists of two stages. In the first stage, key sentences and keywords are extracted from the original documents based on BERT+FC model and BERT+CRF model to filter the redundant information. In the second stage, text matching training and mask training based on specific keywords are carried out according to the extracted elements, and the BERT model is transferred to a semantic representation model through multi-task learning. The experiment results show that the semantic representation scheme can improve the accuracy of matching medical dispute judgment documents to 85.84%.

KW - Elements extraction

KW - Medical dispute

KW - Multi-task learning

KW - Semantic representation

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A Semantic Representation Scheme for Medical Dispute Judgment Documents Based on Elements Extraction

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