Toward an Intelligent Tutoring System for Argument Mining in Legal Texts
Université de Montréal（蒙特利尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Hofstra University（霍夫斯特拉大学）；University of Pittsburgh（匹兹堡大学）
Hannes Westermann, Jaromir Savelka, Vern R. Walker, Kevin D. Ashley, Karim Benyekhlef
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Accepted for presentation at the 35th International Conference on Legal Knowledge and Information Systems (JURIX 2022) and publication in the Frontiers of Artificial Intelligence and Applications series of IOS Press
We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professionals in their work. The results of our experiments focused on the feasibility of the proposed framework are promising. We show that the system is capable of identifying a potential error in the analysis with very low false positives rate (2.0-3.5%), as well as of predicting the key argument element type (e.g., an issue or a holding) with a reasonably high F1-score (0.74).
A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines
In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what th…
University College Dublin（都柏林大学学院）；Birkbeck, University of London（伦敦大学伯贝克学院）
The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of complex annotation work in many interdisciplinary research areas, for example, legal argument mining in digital humanities. Argument mining aims to develop text analysis tools that can automatically retrieve arguments and identify relationships between argumentation clauses. Since argumentation is one of the key aspects of case law, argument mining tools for legal texts are applicable to both academic and non-academi…
Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam（信息科技学院计算机科学系，胡志明市，越南）；Faculty of Information Science and Engineering, University of Information Technology, Ho Chi Minh City, Vietnam（信息科技学院信息科学与工程系，胡志明市，越南）；Laboratory of Multimedia Communications, University of Information Technology, Ho Chi Minh City, Vietnam（信息科技学院多媒体通信实验室，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学，胡志明市，越南）
Mining Legal Arguments to Study Judicial Formalism
Center for Critical Computational Studies（批判性计算研究所以）；Goethe University Frankfurt（法兰克福歌德大学）；Department for Legal Theory and Legal Doctrines, Faculty of Law（法学院法律理论与法律学说系）；Charles University（查尔斯大学）；Technical University of Darmstadt（达姆施塔特技术大学）；Research Center Trustworthy Data Science and Security of the University Alliance Ruhr & Ruhr University Bochum（鲁尔大学博德姆大学可信数据科学与安全研究所以）
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究所）；Computer Science Department, University of California, Los Angeles（加州大学洛杉矶分校计算机科学系）；The MOE Key Laboratory of Computational Linguistics, Peking University（北京大学计算语言学教育部重点实验室）
Causal inference is the process of capturing cause-effect relationship among variables. Most existing works focus on dealing with structured data, while mining causal relationship among factors from unstructured data, like text, has been less examined, but is of great importance, especially in the legal domain. In this paper, we propose a novel Graph-based Causal Inference (GCI) framework, which builds causal graphs from fact descriptions without much human involvement and enables causal inference to facilitate legal practitioners to make proper decisions. We evaluate the framework on a challe…
Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion mining can be enhanced with joint topic modeling, to identify distinct perspectives within the topic, providing an informative overview from unstructured text. We evaluate four joint topic models (TAM, JST, VODUM, and LAM) in a user study assessing human understandability of the extracted perspectives. Based on the results, we conclude that joint topic models such as TAM can discover perspectives that align with hum…
NOWJ@COLIEE 2026: Adaptive Pipelines for Legal Retrieval and Reasoning
VNU University of Engineering and Technology（越南国立工程技术大学）；Center for Juris-Informatics, ROIS-DS College of Engineering & Computer Science, VinUniversity（越南Vin大学工程与计算机科学学院法律信息学中心）
University of Zurich（苏黎世大学）；Thomson Reuters（汤森路透）；Essex University（埃塞克斯大学）；Coburg University of Applied Sciences（科堡应用科学大学）；University of Sheffield（谢菲尔德大学）
Big data, i.e. collecting, storing and processing of data at scale, has recently been possible due to the arrival of clusters of commodity computers powered by application-level distributed parallel operating systems like HDFS/Hadoop/Spark, and such infrastructures have revolutionized data mining at scale. For data mining project to succeed more consistently, some methodologies were developed (e.g. CRISP-DM, SEMMA, KDD), but these do not account for (1) very large scales of processing, (2) dealing with textual (unstructured) data (i.e. Natural Language Processing (NLP, "text analytics"), and (…
