Similar Phrases for Cause of Actions of Civil Cases

• URL: http://arxiv.org/abs/2410.08564v1
• Date: Fri, 11 Oct 2024 06:43:45 GMT
• Title: Similar Phrases for Cause of Actions of Civil Cases
• Authors: Ho-Chien Huang, Chao-Lin Liu,
• Abstract summary: The study implements various similarity measures, including Dice coefficient and Pearson's correlation coefficient. An ensemble model combines rankings, and social network analysis identifies clusters of related COAs.
• Score: 0.5949789346585451
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the Taiwanese judicial system, Cause of Actions (COAs) are essential for identifying relevant legal judgments. However, the lack of standardized COA labeling creates challenges in filtering cases using basic methods. This research addresses this issue by leveraging embedding and clustering techniques to analyze the similarity between COAs based on cited legal articles. The study implements various similarity measures, including Dice coefficient and Pearson's correlation coefficient. An ensemble model combines rankings, and social network analysis identifies clusters of related COAs. This approach enhances legal analysis by revealing inconspicuous connections between COAs, offering potential applications in legal research beyond civil law.

Related papers

• Randomization Techniques to Mitigate the Risk of Copyright Infringement [48.75580082851766]
  We investigate potential randomization approaches that can complement current practices for copyright protection. This is motivated by the inherent ambiguity of the rules that determine substantial similarity in copyright precedents. Similar randomized approaches, such as differential privacy, have been successful in mitigating privacy risks.
  arXiv  Detail & Related papers  (2024-08-21T20:55:00Z)
• Empirical analysis of Binding Precedent efficiency in the Brazilian Supreme Court via Similar Case Retrieval [3.317447070657057]
  We empirically assess the legal impact of five binding precedents, 11, 14, 17, 26 and 37, at the highest court level. We compare the uses of different methods of Natural Language Processing for Similar Case Retrieval. We observe that the deep learning models performed significantly worse in the specific Similar Case Retrieval task.
  arXiv  Detail & Related papers  (2024-07-09T16:17:16Z)
• Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation [14.755026411356315]
  We show how relevance can be learnt automatically in practice with the help of decision trees. We also show that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts.
  arXiv  Detail & Related papers  (2023-10-30T15:01:41Z)
• MUSER: A Multi-View Similar Case Retrieval Dataset [65.36779942237357]
  Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. Existing SCR datasets only focus on the fact description section when judging the similarity between cases. We present M, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations.
  arXiv  Detail & Related papers  (2023-10-24T08:17:11Z)
• An interpretability framework for Similar case matching [8.747801053040256]
  This study proposes an integrated pipeline framework for interpretable SCM. The framework comprises four modules: judicial feature sentence identification, case matching, feature sentence alignment, and conflict resolution. The experimental results show the effectiveness of our proposed framework, establishing a new benchmark for interpretable SCM.
  arXiv  Detail & Related papers  (2023-04-04T08:25:12Z)
• The right to audit and power asymmetries in algorithm auditing [68.8204255655161]
  We elaborate on the challenges and asymmetries mentioned by Sandvig at the IC2S2 2021. We also contribute a discussion of the asymmetries that were not covered by Sandvig. We discuss the implications these asymmetries have for algorithm auditing research.
  arXiv  Detail & Related papers  (2023-02-16T13:57:41Z)
• Equality before the Law: Legal Judgment Consistency Analysis for Fairness [55.91612739713396]
  In this paper, we propose an evaluation metric for judgment inconsistency, Legal Inconsistency Coefficient (LInCo) We simulate judges from different groups with legal judgment prediction (LJP) models and measure the judicial inconsistency with the disagreement of the judgment results given by LJP models trained on different groups. We employ LInCo to explore the inconsistency in real cases and come to the following observations: (1) Both regional and gender inconsistency exist in the legal system, but gender inconsistency is much less than regional inconsistency.
  arXiv  Detail & Related papers  (2021-03-25T14:28:00Z)
• Pairwise Supervision Can Provably Elicit a Decision Boundary [84.58020117487898]
  Similarity learning is a problem to elicit useful representations by predicting the relationship between a pair of patterns. We show that similarity learning is capable of solving binary classification by directly eliciting a decision boundary.
  arXiv  Detail & Related papers  (2020-06-11T05:35:16Z)
• Methods for Computing Legal Document Similarity: A Comparative Study [9.007583099505954]
  Finding similar legal documents is an important and challenging task in the domain of Legal Information Retrieval. We propose two broad ways of measuring similarity between legal documents - analyzing the precedent citation network, and measuring similarity based on textual content similarity measures. We explore two promising new similarity computation methods - one text-based and the other based on network embeddings, which have not been considered till now.
  arXiv  Detail & Related papers  (2020-04-26T08:26:04Z)
• Fair Correlation Clustering [92.15492066925977]
  We obtain approximation algorithms for correlation clustering under several important types of fairness constraints. We show that fair solutions to correlation clustering can be obtained with limited increase in cost compared to the state-of-the-art (unfair) algorithms.
  arXiv  Detail & Related papers  (2020-02-06T14:28:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.