LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases
- URL: http://arxiv.org/abs/2501.14114v1
- Date: Thu, 23 Jan 2025 22:10:00 GMT
- Title: LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases
- Authors: T. Y. S. S. Santosh, Isaac Misael OlguĂn Nolasco, Matthias Grabmair,
- Abstract summary: We propose LeCoPCR, a novel approach that explicitly generate intents in the form of legal concepts from a given query case facts.
We employ a weak supervision approach to extract key legal concepts from the reasoning section using Determinantal Point Process (DPP) to balance quality and diversity.
- Score: 1.3723120574076126
- License:
- Abstract: Prior case retrieval (PCR) is crucial for legal practitioners to find relevant precedent cases given the facts of a query case. Existing approaches often overlook the underlying semantic intent in determining relevance with respect to the query case. In this work, we propose LeCoPCR, a novel approach that explicitly generate intents in the form of legal concepts from a given query case facts and then augments the query with these concepts to enhance models understanding of semantic intent that dictates relavance. To overcome the unavailability of annotated legal concepts, we employ a weak supervision approach to extract key legal concepts from the reasoning section using Determinantal Point Process (DPP) to balance quality and diversity. Experimental results on the ECtHR-PCR dataset demonstrate the effectiveness of leveraging legal concepts and DPP-based key concept extraction.
Related papers
- Rethinking State Disentanglement in Causal Reinforcement Learning [78.12976579620165]
Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered through identifiability.
We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states.
We propose a novel approach for general partially observable Markov Decision Processes (POMDPs) by replacing the complicated structural constraints in previous methods with two simple constraints for transition and reward preservation.
arXiv Detail & Related papers (2024-08-24T06:49:13Z) - ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights [1.3723120574076126]
We develop a prior case retrieval dataset based on judgements from the European Court of Human Rights (ECtHR)
We benchmark different lexical and dense retrieval approaches with various negative sampling strategies.
We find that difficulty-based negative sampling strategies were not effective for the PCR task.
arXiv Detail & Related papers (2024-03-31T08:06:54Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval [18.058942674792604]
We propose a novel few-shot workflow tailored to the relevant judgment of legal cases.
By comparing the relevance judgments of LLMs and human experts, we empirically show that we can obtain reliable relevance judgments.
arXiv Detail & Related papers (2024-03-27T09:46:56Z) - Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning [49.23103067844278]
We propose the task of multi-defendant LJP, which aims to automatically predict the judgment results for each defendant of multi-defendant cases.
Two challenges arise with the task of multi-defendant LJP: (1) indistinguishable judgment results among various defendants; and (2) the lack of a real-world dataset for training and evaluation.
arXiv Detail & Related papers (2023-12-10T04:46:30Z) - 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) - SAILER: Structure-aware Pre-trained Language Model for Legal Case
Retrieval [75.05173891207214]
Legal case retrieval plays a core role in the intelligent legal system.
Most existing language models have difficulty understanding the long-distance dependencies between different structures.
We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
arXiv Detail & Related papers (2023-04-22T10:47:01Z) - Legal Element-oriented Modeling with Multi-view Contrastive Learning for
Legal Case Retrieval [3.909749182759558]
We propose an interaction-focused network for legal case retrieval with a multi-view contrastive learning objective.
Case-view contrastive learning minimizes the hidden space distance between relevant legal case representations.
We employ a legal element knowledge-aware indicator to detect legal elements of cases.
arXiv Detail & Related papers (2022-10-11T06:47:23Z) - A Principled Design of Image Representation: Towards Forensic Tasks [75.40968680537544]
We investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application.
At the theoretical level, we propose a new representation framework for forensics, called Dense Invariant Representation (DIR), which is characterized by stable description with mathematical guarantees.
We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors.
arXiv Detail & Related papers (2022-03-02T07:46:52Z) - Causal Inference Principles for Reasoning about Commonsense Causality [93.19149325083968]
Commonsense causality reasoning aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person.
Existing work usually relies on deep language models wholeheartedly, and is potentially susceptible to confounding co-occurrences.
Motivated by classical causal principles, we articulate the central question of CCR and draw parallels between human subjects in observational studies and natural languages.
We propose a novel framework, ROCK, to Reason O(A)bout Commonsense K(C)ausality, which utilizes temporal signals as incidental supervision.
arXiv Detail & Related papers (2022-01-31T06:12:39Z)
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.