Argumentation-Driven Evidence Association in Criminal Cases

Yefei Teng; Wenhan Chao

doi:10.18653/v1/2021.findings-emnlp.257

Argumentation-Driven Evidence Association in Criminal Cases

Yefei Teng
, Wenhan Chao

计算机学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.

源语言	英语
主期刊名	Findings of the Association for Computational Linguistics, Findings of ACL
主期刊副标题	EMNLP 2021
编辑	Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-Tau Yih
出版商	Association for Computational Linguistics (ACL)
页	2997-3001
页数	5
ISBN（电子版）	9781955917100
DOI	https://doi.org/10.18653/v1/2021.findings-emnlp.257
出版状态	已出版 - 2021
活动	2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 - Punta Cana, 多米尼加共和国期限: 7 11月 2021 → 11 11月 2021

出版系列

姓名	Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021

会议

会议	2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021
国家/地区	多米尼加共和国
市	Punta Cana
时期	7/11/21 → 11/11/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 16 和平、正义和强大机构

访问文件

10.18653/v1/2021.findings-emnlp.257

其它文件与链接

链接到 Scopus 的出版物

引用此

Teng, Y., & Chao, W. (2021). Argumentation-Driven Evidence Association in Criminal Cases. 在 M.-F. Moens, X. Huang, L. Specia, & S. W.-T. Yih (编辑), Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 (页码 2997-3001). (Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.257

Teng, Yefei ; Chao, Wenhan. / Argumentation-Driven Evidence Association in Criminal Cases. Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021. 编辑 / Marie-Francine Moens ; Xuanjing Huang ; Lucia Specia ; Scott Wen-Tau Yih. Association for Computational Linguistics (ACL), 2021. 页码 2997-3001 (Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021).

@inproceedings{59f17145a2e549b895da83e67e286b3a,

title = "Argumentation-Driven Evidence Association in Criminal Cases",

abstract = "Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.",

author = "Yefei Teng and Wenhan Chao",

note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 ; Conference date: 07-11-2021 Through 11-11-2021",

year = "2021",

doi = "10.18653/v1/2021.findings-emnlp.257",

language = "英语",

series = "Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021",

publisher = "Association for Computational Linguistics (ACL)",

pages = "2997--3001",

editor = "Marie-Francine Moens and Xuanjing Huang and Lucia Specia and Yih, \{Scott Wen-Tau\}",

booktitle = "Findings of the Association for Computational Linguistics, Findings of ACL",

address = "澳大利亚",

}

Teng, Y & Chao, W 2021, Argumentation-Driven Evidence Association in Criminal Cases. 在 M-F Moens, X Huang, L Specia & SW-T Yih (编辑), Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021. Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, Association for Computational Linguistics (ACL), 页码 2997-3001, 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, Punta Cana, 多米尼加共和国, 7/11/21. https://doi.org/10.18653/v1/2021.findings-emnlp.257

Argumentation-Driven Evidence Association in Criminal Cases. / Teng, Yefei; Chao, Wenhan.
Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021. 编辑 / Marie-Francine Moens; Xuanjing Huang; Lucia Specia; Scott Wen-Tau Yih. Association for Computational Linguistics (ACL), 2021. 页码 2997-3001 (Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Argumentation-Driven Evidence Association in Criminal Cases

AU - Teng, Yefei

AU - Chao, Wenhan

PY - 2021

Y1 - 2021

N2 - Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.

AB - Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.

UR - https://www.scopus.com/pages/publications/85129136155

U2 - 10.18653/v1/2021.findings-emnlp.257

DO - 10.18653/v1/2021.findings-emnlp.257

M3 - 会议稿件

AN - SCOPUS:85129136155

T3 - Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021

SP - 2997

EP - 3001

BT - Findings of the Association for Computational Linguistics, Findings of ACL

A2 - Moens, Marie-Francine

A2 - Huang, Xuanjing

A2 - Specia, Lucia

A2 - Yih, Scott Wen-Tau

PB - Association for Computational Linguistics (ACL)

T2 - 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021

Y2 - 7 November 2021 through 11 November 2021

ER -

Teng Y, Chao W. Argumentation-Driven Evidence Association in Criminal Cases. 在 Moens MF, Huang X, Specia L, Yih SWT, 编辑, Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021. Association for Computational Linguistics (ACL). 2021. 页码 2997-3001. (Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021). doi: 10.18653/v1/2021.findings-emnlp.257

Argumentation-Driven Evidence Association in Criminal Cases

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此