Artificial Intelligence and Law最新文献_第2页

Automated legal reasoning with discretion to act using s(LAW) 使用 s（LAW）进行自动法律推理并酌情采取行动

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-11-20 DOI: 10.1007/s10506-023-09376-5

Joaquín Arias, Mar Moreno-Rebato, Jose A. Rodriguez-García, Sascha Ossowski

引用次数: 0

Bringing order into the realm of Transformer-based language models for artificial intelligence and law 将秩序带入基于 Transformer 的人工智能和法律语言模型领域

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-11-20 DOI: 10.1007/s10506-023-09374-7

Candida M. Greco, Andrea Tagarelli

{"title":"Bringing order into the realm of Transformer-based language models for artificial intelligence and law","authors":"Candida M. Greco, Andrea Tagarelli","doi":"10.1007/s10506-023-09374-7","DOIUrl":"10.1007/s10506-023-09374-7","url":null,"abstract":"<div><p>Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and understanding. Like for other textual domains, TLMs have indeed pushed the state-of-the-art of AI approaches for many tasks of interest in the legal domain. Despite the first Transformer model being proposed about six years ago, there has been a rapid progress of this technology at an unprecedented rate, whereby BERT and related models represent a major reference, also in the legal domain. This article provides the first systematic overview of TLM-based methods for AI-driven problems and tasks in the legal sphere. A major goal is to highlight research advances in this field so as to understand, on the one hand, how the Transformers have contributed to the success of AI in supporting legal processes, and on the other hand, what are the current limitations and opportunities for further research development.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 4","pages":"863 - 1010"},"PeriodicalIF":3.1,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09374-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519046","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

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