Attorneys and others have a strong interest in having a digital library with suitable services (e.g., summarizing, searching, and browsing) to help them work with large corpora of legal depositions. Their needs often involve understanding the semantics of such documents. That depends in part on the role of the deponent, e.g., plaintiff, defendant, law enforcement personnel, expert, etc. In the case of tort litigation associated with property and casualty insurance claims, such as relating to an injury, it is important to know not only about liability, but also about events, accidents, physical conditions, and treatments.
We hypothesize that a legal deposition consists of various aspects that are discussed as part of the deponent testimony. Accordingly, we developed an ontology of aspects in a legal deposition for accident and injury cases. Using that, we have developed a classifier that can identify portions of text for each of the aspects of interest. Doing so was complicated by the peculiarities of this genre, e.g., that deposition transcripts generally consist of data in the form of question-answer (QA) pairs. Accordingly, our automated system starts with pre-processing, and then transforms the QA pairs into a canonical form made up of declarative sentences. Classifying the declarative sentences that are generated, according to the aspect, can then help with downstream tasks such as summarization, segmentation, question-answering, and information retrieval.
Our methods have achieved a classification F1 score of 0.83. Having the aspects classified with a good accuracy will help in choosing QA pairs that can be used as candidate summary sentences, and to generate an informative summary for legal professionals or insurance claim agents. Our methodology could be extended to legal depositions of other kinds, and to aid services like searching.
In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this rese…
Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment
VNU University of Engineering and Technology（越南国立大学工程技术大学）；National Institute of Informatics（国立信息学研究所）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）
University of Moratuwa（莫拉图瓦大学）；University of Colombo（科伦坡大学）
Aspect-Based Sentiment Analysis (ABSA) has been prominent and ongoing research over many different domains, but it is not widely discussed in the legal domain. A number of publicly available datasets for a wide range of domains usually fulfill the needs of researchers to perform their studies in the field of ABSA. To the best of our knowledge, there is no publicly available dataset for the Aspect (Party) Based Sentiment Analysis for legal opinion texts. Therefore, creating a publicly available dataset for the research of ABSA for the legal domain can be considered as a task with significant im…
Despite the 'apparent clarity' of a given legal provision, its application may result in an outcome that does not exactly conform to the semantic level of a statute. The vagueness within a legal text is induced intentionally to accommodate all possible scenarios under which such norms should be applied, thus making the role of pragmatics an important aspect also in the representation of a legal norm and reasoning on top of it. The notion of pragmatics considered in this paper does not focus on the aspects associated with judicial decision making. The paper aims to shed light on the aspects of …
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
School of Mathematical Sciences（数学科学学院）；Tel Aviv University（特拉维夫大学）；Department of Electrical Engineering（电气工程系）；The Faculty of Engineering（工程学院）；Bar Ilan University（巴伊兰大学）
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?
Centre for Responsible AI, Indian Institute of Technology Madras（印度马德拉斯理工学院负责任人工智能中心）；International Institute of Information Technology, Hyderabad（海德拉巴国际信息技术学院）；AmexAI Labs, American Express（美国运通AmexAI实验室）；Wadhwani School of Data Science and AI, Indian Institute of Technology Madras（印度马德拉斯理工学院瓦德瓦尼数据科学与人工智能学院）
MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documents
University of Utah（犹他大学）；Bosch Research（博世研究院）；Indian Institute of Technology Kharagpur（印度克勒格布尔理工学院）；Indian Institute of Science Education and Research Kolkata（印度加尔各答科学教育与研究学院）
An interpretability framework for Similar case matching
Guangdong University of Technology（广东工业大学）；KTH Royal Institute of Technology（KTH皇家理工学院）；Guangdong University of Foreign Studies（广东外语外贸大学）；Lingnan Normal University（岭南师范学院）
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification
Graduate Institute of International and Development Studies（国际与发展研究研究生院）；Faculty of Law, Ruhr University Bochum（波鸿鲁尔大学法学院）；IT University of Copenhagen（哥本哈根信息技术大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑工业大学）
