Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks

• URL: http://arxiv.org/abs/2301.12847v1
• Date: Mon, 30 Jan 2023 12:59:09 GMT
• Title: Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
• Authors: Antoine Louis, Gijs van Dijck, Gerasimos Spanakis
• Abstract summary: We propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.
• Score: 3.5880535198436156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.

Related papers

• Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs [67.54302101989542]
  Legal case retrieval aims to provide similar cases as references for a given fact description. Existing works mainly focus on case-to-case retrieval using lengthy queries. Data scale is insufficient to satisfy the training requirements of existing data-hungry neural models.
  arXiv  Detail & Related papers  (2024-10-09T06:26:39Z)
• CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation [44.67578050648625]
  We transform a large open-source legal corpus into a dataset supporting information retrieval (IR) and retrieval-augmented generation (RAG) This dataset CLERC is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations into a cogent analysis that supports a reasoning goal.
  arXiv  Detail & Related papers  (2024-06-24T23:57:57Z)
• Judgement Citation Retrieval using Contextual Similarity [0.0]
  We propose a methodology that combines natural language processing (NLP) and machine learning techniques to enhance the organization and utilization of legal case descriptions. Our methodology addresses two primary objectives: unsupervised clustering and supervised citation retrieval. Our methodology achieved an impressive accuracy rate of 90.9%.
  arXiv  Detail & Related papers  (2024-05-28T04:22:28Z)
• 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)
• Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions [0.16385815610837165]
  The size of legal opinions continues to grow, making it increasingly challenging to develop a model that can accurately predict the rhetorical roles of legal opinions. We propose a novel model architecture for automatically predicting rhetorical roles using pre-trained language models (PLMs) enhanced with knowledge of sentence position information. Based on an annotated corpus from the LegalEval@SemEval2023 competition, we demonstrate that our approach requires fewer parameters, resulting in lower computational costs.
  arXiv  Detail & Related papers  (2023-10-08T20:33:55Z)
• Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs [5.168558598888541]
  This paper presents a knowledge graph construction method for legal case documents and related laws. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment.
  arXiv  Detail & Related papers  (2023-09-16T18:31:47Z)
• 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)
• Attentive Deep Neural Networks for Legal Document Retrieval [2.4350217735794337]
  We study the use of attentive neural network-based text representation for statute law document retrieval. We develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. Experimental results show that Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages.
  arXiv  Detail & Related papers  (2022-12-13T01:37:27Z)
• 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)
• Text-guided Legal Knowledge Graph Reasoning [11.089663225933412]
  We propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP.
  arXiv  Detail & Related papers  (2021-04-06T04:42:56Z)
• Learning Contextualized Document Representations for Healthcare Answer Retrieval [68.02029435111193]
  Contextual Discourse Vectors (CDV) is a distributed document representation for efficient answer retrieval from long documents. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking.
  arXiv  Detail & Related papers  (2020-02-03T15:47:19Z)

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.