Automating petition classification in Brazil’s legal system: a two-step deep learning approach

IF 3.1 2区 社会学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuri D. R. Costa, Hugo Oliveira, Valério Nogueira Jr., Lucas Massa, Xu Yang, Adriano Barbosa, Krerley Oliveira, Thales Vieira
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Abstract

Automated classification of legal documents has been the subject of extensive research in recent years. However, this is still a challenging task for long documents, since it is difficult for a model to identify the most relevant information for classification. In this paper, we propose a two-stage supervised learning approach for the classification of petitions, a type of legal document that requests a court order. The proposed approach is based on a word-level encoder–decoder Seq2Seq deep neural network, such as a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Encoder Representations from Transformers (BERT) model, and a document-level Support Vector Machine classifier. To address the challenges posed by the lengthy legal documents, the approach introduces a human-in-the-loop approach, whose task is to localize and tag relevant segments of text in the word-level training part, which dramatically reduces the dimension of the document classifier input vector. We performed experiments to validate our approach using a real-world dataset comprised of 270 intermediate petitions, which were carefully annotated by specialists from the 15th civil unit of the State of Alagoas, Brazil. Our results revealed that both BiLSTM and BERT-Convolutional Neural Networks variants achieved an accuracy of up to 95.49%, and also outperformed baseline classifiers based on the Term Frequency–Inverse Document Frequency test vectorizer. The proposed approach is currently being utilized to automate the aforementioned justice unit, thereby increasing its efficiency in handling repetitive tasks.

Abstract Image

巴西法律系统中的请愿分类自动化:两步式深度学习方法
法律文件的自动分类是近年来广泛研究的课题。然而,对于长文档来说,这仍然是一项具有挑战性的任务,因为模型很难识别最相关的信息进行分类。在本文中,我们提出了一种两阶段监督学习方法来分类请愿书,这是一种要求法院命令的法律文件。该方法基于词级编码器-解码器Seq2Seq深度神经网络,如双向长短期记忆(BiLSTM)或双向编码器变形表示(BERT)模型,以及文档级支持向量机分类器。为了解决冗长的法律文件所带来的挑战,该方法引入了一种human-in-the-loop方法,其任务是在单词级训练部分对文本的相关片段进行定位和标记,从而大大降低了文档分类器输入向量的维数。我们使用由270份中间请愿书组成的真实数据集进行了实验,验证了我们的方法,这些请愿书由巴西阿拉戈斯州第15民事单位的专家仔细注释。我们的研究结果表明,BiLSTM和bert -卷积神经网络变体的准确率都达到了95.49%,并且也优于基于术语频率-逆文档频率测试矢量器的基线分类器。目前正在利用拟议的办法使上述司法单位实现自动化,从而提高其处理重复性任务的效率。
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来源期刊
CiteScore
9.50
自引率
26.80%
发文量
33
期刊介绍: Artificial Intelligence and Law is an international forum for the dissemination of original interdisciplinary research in the following areas: Theoretical or empirical studies in artificial intelligence (AI), cognitive psychology, jurisprudence, linguistics, or philosophy which address the development of formal or computational models of legal knowledge, reasoning, and decision making. In-depth studies of innovative artificial intelligence systems that are being used in the legal domain. Studies which address the legal, ethical and social implications of the field of Artificial Intelligence and Law. Topics of interest include, but are not limited to, the following: Computational models of legal reasoning and decision making; judgmental reasoning, adversarial reasoning, case-based reasoning, deontic reasoning, and normative reasoning. Formal representation of legal knowledge: deontic notions, normative modalities, rights, factors, values, rules. Jurisprudential theories of legal reasoning. Specialized logics for law. Psychological and linguistic studies concerning legal reasoning. Legal expert systems; statutory systems, legal practice systems, predictive systems, and normative systems. AI and law support for legislative drafting, judicial decision-making, and public administration. Intelligent processing of legal documents; conceptual retrieval of cases and statutes, automatic text understanding, intelligent document assembly systems, hypertext, and semantic markup of legal documents. Intelligent processing of legal information on the World Wide Web, legal ontologies, automated intelligent legal agents, electronic legal institutions, computational models of legal texts. Ramifications for AI and Law in e-Commerce, automatic contracting and negotiation, digital rights management, and automated dispute resolution. Ramifications for AI and Law in e-governance, e-government, e-Democracy, and knowledge-based systems supporting public services, public dialogue and mediation. Intelligent computer-assisted instructional systems in law or ethics. Evaluation and auditing techniques for legal AI systems. Systemic problems in the construction and delivery of legal AI systems. Impact of AI on the law and legal institutions. Ethical issues concerning legal AI systems. In addition to original research contributions, the Journal will include a Book Review section, a series of Technology Reports describing existing and emerging products, applications and technologies, and a Research Notes section of occasional essays posing interesting and timely research challenges for the field of Artificial Intelligence and Law. Financial support for the Journal of Artificial Intelligence and Law is provided by the University of Pittsburgh School of Law.
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