Artificial Intelligence and Law最新文献_第4页

Natural language processing for legal document review: categorising deontic modalities in contracts 法律文件审查的自然语言处理：合同中道义模式的分类

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-11-11 DOI: 10.1007/s10506-023-09379-2

S. Georgette Graham, Hamidreza Soltani, Olufemi Isiaq

引用次数: 0

A large scale benchmark for session-based recommendations on the legal domain 基于会议的法律领域建议的大规模基准

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-10-25 DOI: 10.1007/s10506-023-09378-3

Marcos Aurélio Domingues, Edleno Silva de Moura, Leandro Balby Marinho, Altigran da Silva

{"title":"A large scale benchmark for session-based recommendations on the legal domain","authors":"Marcos Aurélio Domingues, Edleno Silva de Moura, Leandro Balby Marinho, Altigran da Silva","doi":"10.1007/s10506-023-09378-3","DOIUrl":"10.1007/s10506-023-09378-3","url":null,"abstract":"<div><p>The proliferation of legal documents in various formats and their dispersion across multiple courts present a significant challenge for users seeking precise matches to their information requirements. Despite notable advancements in legal information retrieval systems, research into legal recommender systems remains limited. A plausible factor contributing to this scarcity could be the absence of extensive publicly accessible datasets or benchmarks. While a few studies have emerged in this field, a comprehensive analysis of the distinct attributes of legal data that influence the design of effective legal recommenders is notably absent in the current literature. This paper addresses this gap by initially amassing a comprehensive session-based dataset from Jusbrasil, one of Brazil’s largest online legal platforms. Subsequently, we scrutinize and discourse key facets of legal session-based recommendation data, including session duration, types of recommendable legal artifacts, coverage, and popularity. Furthermore, we introduce the first session-based recommendation benchmark tailored to the legal domain, shedding light on the performance and constraints of several renowned session-based recommendation approaches. These evaluations are based on real-world data sourced from Jusbrasil.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"43 - 78"},"PeriodicalIF":3.1,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating legal event and context information for Chinese similar case analysis 整合法律事件与语境信息进行中国同类案例分析

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-10-25 DOI: 10.1007/s10506-023-09377-4

Jingpei Dan, Lanlin Xu, Yuming Wang

{"title":"Integrating legal event and context information for Chinese similar case analysis","authors":"Jingpei Dan, Lanlin Xu, Yuming Wang","doi":"10.1007/s10506-023-09377-4","DOIUrl":"10.1007/s10506-023-09377-4","url":null,"abstract":"<div><p>Similar case analysis (SCA) is an essential topic in legal artificial intelligence, serving as a reference for legal professionals. Most existing works treat SCA as a traditional text classification task and ignore some important legal elements that affect the verdict and case similarity, like legal events, and thus are easily misled by semantic structure. To address this issue, we propose a Legal Event-Context Model named LECM to improve the accuracy and interpretability of SCA based on Chinese legal corpus. The event-context integration mechanism, which is an essential component of the LECM, is proposed to integrate the legal event and context information based on the attention mechanism, enabling legal events to be associated with their corresponding relevant contexts. We introduce an event detection module to obtain the legal event information, which is pre-trained on a legal event detection dataset to avoid labeling events manually. We conduct extensive experiments on two SCA tasks, i.e., similar case matching (SCM) and similar case retrieval (SCR). Compared with baseline models, LECM is validated by about 13% and 11% average improvement in terms of mean average precision and accuracy respectively, for SCR and SCM tasks. These results indicate that LECM effectively utilizes event-context knowledge to enhance SCA performance and its potential application in various legal document analysis tasks.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"1 - 42"},"PeriodicalIF":3.1,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel network-based paragraph filtering technique for legal document similarity analysis 一种新的基于网络的法律文件相似度分析段落过滤技术

2区社会学

Artificial Intelligence and Law Pub Date : 2023-10-19 DOI: 10.1007/s10506-023-09375-6

Mayur Makawana, Rupa G. Mehta

引用次数: 0

Multi-language transfer learning for low-resource legal case summarization 针对低资源法律案例摘要的多语言迁移学习

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-09-25 DOI: 10.1007/s10506-023-09373-8

Gianluca Moro, Nicola Piscaglia, Luca Ragazzi, Paolo Italiani

引用次数: 0

Ant: a process aware annotation software for regulatory compliance Ant：用于法规遵从性的流程感知注释软件

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-08-09 DOI: 10.1007/s10506-023-09372-9

Raphaël Gyory, David Restrepo Amariles, Gregory Lewkowicz, Hugues Bersini

{"title":"Ant: a process aware annotation software for regulatory compliance","authors":"Raphaël Gyory, David Restrepo Amariles, Gregory Lewkowicz, Hugues Bersini","doi":"10.1007/s10506-023-09372-9","DOIUrl":"10.1007/s10506-023-09372-9","url":null,"abstract":"<div><p>Accurate data annotation is essential to successfully implementing machine learning (ML) for regulatory compliance. Annotations allow organizations to train supervised ML algorithms and to adapt and audit the software they buy. The lack of annotation tools focused on regulatory data is slowing the adoption of established ML methodologies and process models, such as CRISP-DM, in various legal domains, including in regulatory compliance. This article introduces Ant, an open-source annotation software for regulatory compliance. Ant is designed to adapt to complex organizational processes and enable compliance experts to be in control of ML projects. By drawing on Business Process Modeling (BPM), we show that Ant can contribute to lift major technical bottlenecks to effectively implement regulatory compliance through software, such as the access to multiple sources of heterogeneous data and the integration of process complexities in the ML pipeline. We provide empirical data to validate the performance of Ant, illustrate its potential to speed up the adoption of ML in regulatory compliance, and highlight its limitations.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 4","pages":"1075 - 1110"},"PeriodicalIF":3.1,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09372-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42450021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lessons learned building a legal inference dataset 构建法律推理数据集的经验教训

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-31 DOI: 10.1007/s10506-023-09370-x

Sungmi Park, Joshua I. James

引用次数: 0

A topic discovery approach for unsupervised organization of legal document collections 一种无监督组织法律文件集合的主题发现方法

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-19 DOI: 10.1007/s10506-023-09371-w

Daniela Vianna, Edleno Silva de Moura, Altigran Soares da Silva

{"title":"A topic discovery approach for unsupervised organization of legal document collections","authors":"Daniela Vianna, Edleno Silva de Moura, Altigran Soares da Silva","doi":"10.1007/s10506-023-09371-w","DOIUrl":"10.1007/s10506-023-09371-w","url":null,"abstract":"<div><p>Technology has substantially transformed the way legal services operate in many different countries. With a large and complex collection of digitized legal documents, the judiciary system worldwide presents a promising scenario for the development of intelligent tools. In this work, we tackle the challenging task of organizing and summarizing the constantly growing collection of legal documents, uncovering hidden topics, or themes that later can support tasks such as legal case retrieval and legal judgment prediction. Our approach to this problem relies on topic discovery techniques combined with a variety of preprocessing techniques and learning-based vector representations of words, such as Doc2Vec and BERT-like models. The proposed method was validated using four different datasets composed of short and long legal documents in Brazilian Portuguese, from legal decisions to chapters in legal books. Analysis conducted by a team of legal specialists revealed the effectiveness of the proposed approach to uncover unique and relevant topics from large collections of legal documents, serving many purposes, such as giving support to legal case retrieval tools and also providing the team of legal specialists with a tool that can accelerate their work of labeling/tagging legal documents.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 4","pages":"1045 - 1074"},"PeriodicalIF":3.1,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46420377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model 利用大规模预训练语言模型构建法律题库，将法律知识带给大众

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-06 DOI: 10.1007/s10506-023-09367-6

Mingruo Yuan, Ben Kao, Tien-Hsuan Wu, Michael M. K. Cheung, Henry W. H. Chan, Anne S. Y. Cheung, Felix W. H. Chan, Yongxi Chen

{"title":"Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model","authors":"Mingruo Yuan, Ben Kao, Tien-Hsuan Wu, Michael M. K. Cheung, Henry W. H. Chan, Anne S. Y. Cheung, Felix W. H. Chan, Yongxi Chen","doi":"10.1007/s10506-023-09367-6","DOIUrl":"10.1007/s10506-023-09367-6","url":null,"abstract":"<div><p>Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson’s terms. Second, we construct a <i>Legal Question Bank</i>, which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive <i>CLIC Recommender</i>. Given a user’s verbal description of a legal situation that requires a legal solution, CRec interprets the user’s input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions against human-composed questions and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"769 - 805"},"PeriodicalIF":3.1,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42058228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

M-LAMAC: a model for linguistic assessment of mitigating and aggravating circumstances of criminal responsibility using computing with words M-LAMAC:一个使用词计算的刑事责任减轻和加重情况的语言评估模型

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-04 DOI: 10.1007/s10506-023-09365-8

Carlos Rafael Rodríguez Rodríguez, Yarina Amoroso Fernández, Denis Sergeevich Zuev, Marieta Peña Abreu, Yeleny Zulueta Veliz

{"title":"M-LAMAC: a model for linguistic assessment of mitigating and aggravating circumstances of criminal responsibility using computing with words","authors":"Carlos Rafael Rodríguez Rodríguez, Yarina Amoroso Fernández, Denis Sergeevich Zuev, Marieta Peña Abreu, Yeleny Zulueta Veliz","doi":"10.1007/s10506-023-09365-8","DOIUrl":"10.1007/s10506-023-09365-8","url":null,"abstract":"<div><p>The general mitigating and aggravating circumstances of criminal liability are elements attached to the crime that, when they occur, affect the punishment quantum. Cuban criminal legislation provides a catalog of such circumstances and some general conditions for their application. Such norms give judges broad discretion in assessing circumstances and adjusting punishment based on the intensity of those circumstances. In the interest of broad judicial discretion, the law does not establish specific ways for measuring circumstances’ intensity. This gives judges more freedom and autonomy, but it also imposes on them more social responsibility and challenges them to manage the uncertainty and subjectivity inherent in this complex activity. This paper proposes a model to aid the linguistic assessment of circumstances’ intensity and to provide linguistic and numerical recommendations to determine an appropriate punishment interval. M-LAMAC determines the collective evaluation of circumstances of the same type, determines the prevalence of a type of circumstance by means of a compensation function, recommends the required modification in the input interval, and finally recommends a numerical interval adjusted to the judges’ initially expressed preferences. The model’s applicability is demonstrated by means of several experiments on a fictitious case of bank document forgery.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"697 - 739"},"PeriodicalIF":3.1,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48842789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0