Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models.

Overview

Statutory Interpretation Data Set

This repository contains the data set created for the following research papers:

Savelka, Jaromir, and Kevin D. Ashley. "Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models." Findings of the Association for Computational Linguistics: EMNLP 2021. 2021.

Jaromir Savelka, Huihui Xu, and Kevin D. Ashley. 2019. Improving Sentence Retrieval from Case Law for Statutory Interpretation. In Seventeenth International Conference on Artificial Intelligence and Law (ICAIL ’19), June 17–21, 2019, Montreal, QC, Canada, Floris Bex (Ed.). ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3322640.3326736

Task

Given a statutory provision, user's interest in the meaning of a phrase from the provision, and a list of sentences we would like to rank more highly the sentences that elaborate upon the meaning of the statutory phrase of interest, such as:

  • definitional sentences (e.g., a sentence that provides a test for when the phrase applies)
  • sentences that state explicitly in a different way what the statutory phrase means or state what it does not mean
  • sentences that provide an example, instance, or counterexample of the phrase
  • sentences that show how a court determines whether something is such an example, instance, or counterexample.

Corpus Overview

For this corpus we selected fourty two terms from different provisions of the United States Code.

For each term we have collected a set of sentences by extracting all the sentences mentioning the term from the court decisions retrieved from the Caselaw access project data.

In total the corpus consists of 26,959 sentences.

The sentences are classified into four categories according to their usefulness for the interpretation:

  • high value - sentence intended to define or elaborate on the meaning of the term
  • certain value - sentence that provides grounds to elaborate on the term's meaning
  • potential value - sentence that provides additional information beyond what is known from the provision the term comes from
  • no value - no additional information over what is known from the provision

See Annotation guidelines for additional details.

Data Structure

Each zip file contains data related to one of the fourty two queries. There are four files in total containing the texts of different granularity. These allow to replicate experiments reported in the paper cited above.

  • case
    • original_id - case id from Caselaw access project
    • name
    • short_name
    • date
    • official_date
    • official citation
    • alternate_citations
    • court
    • short_court - court abbreviation
    • jurisdiction
    • short_jurisdiction - jurisdiction abbreviation
    • attorneys
    • parties
    • judges
    • text
  • opinion
    • case_id - pointer to the case the opinion belongs to
    • author
    • type - e.g., concurrence, dissent
    • position - position of the opinion within the case
    • text
  • paragraph
    • case_id - pointer to the case the opinion belongs to
    • opinion_id - pointer to the opinion the paragraph belongs to
    • position - position of the paragraph within the opinion
    • text
  • sentence
    • case_id - pointer to the case the sentence belongs to
    • opinion_id - pointer to the opinion the sentence belongs to
    • paragraph_id - pointer to the paragraph the sentence belongs to
    • position - position of the sentence within the paragraph
    • text
    • label - human-created gold label of the sentence value

Terms of Use

For use of the data we kindly ask you to provide the two following attributions:

Savelka, Jaromir, and Kevin D. Ashley. "Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models." Findings of the Association for Computational Linguistics: EMNLP 2021. 2021.

The President and Fellows of Harvard University, Caselaw access project, Caselaw access project, 2018.

Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
An executor that performs image segmentation on fashion items

ClothingSegmenter U2NET fashion image/clothing segmenter based on https://github.com/levindabhi/cloth-segmentation Overview The ClothingSegmenter exec

Jina AI 5 Mar 30, 2022
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

MADGRAD Optimization Method A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization pip install madgrad Try it out! A best

Meta Research 774 Dec 31, 2022
The coda and data for "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach" (ACL '21)

We propose a hierarchical core-fringe learning framework to measure fine-grained domain relevance of terms – the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., de

Jie Huang 14 Oct 21, 2022
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

7.7k Jan 03, 2023
SoK: Vehicle Orientation Representations for Deep Rotation Estimation

SoK: Vehicle Orientation Representations for Deep Rotation Estimation Raymond H. Tu, Siyuan Peng, Valdimir Leung, Richard Gao, Jerry Lan This is the o

FIRE Capital One Machine Learning of the University of Maryland 12 Oct 07, 2022
Implementation of U-Net and SegNet for building segmentation

Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te

Martin.w-e 3 Dec 07, 2022
Re-implement CycleGAN in Tensorlayer

CycleGAN_Tensorlayer Re-implement CycleGAN in TensorLayer Original CycleGAN Improved CycleGAN with resize-convolution Prerequisites: TensorLayer Tenso

89 Aug 15, 2022
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based

Timo Schick 62 Dec 12, 2022
An open-source Deep Learning Engine for Healthcare that aims to treat & prevent major diseases

AlphaCare Background AlphaCare is a work-in-progress, open-source Deep Learning Engine for Healthcare that aims to treat and prevent major diseases. T

Siraj Raval 44 Nov 05, 2022
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc

Matt Cooper 704 Nov 26, 2022
Object DGCNN and DETR3D, Our implementations are built on top of MMdetection3D.

This repo contains the implementations of Object DGCNN (https://arxiv.org/abs/2110.06923) and DETR3D (https://arxiv.org/abs/2110.06922). Our implementations are built on top of MMdetection3D.

Wang, Yue 539 Jan 07, 2023
Coded illumination for improved lensless imaging

CodedCam Coded Illumination for Improved Lensless Imaging Paper | Supplementary results | Data and Code are available. Coded illumination for improved

Computational Sensing and Information Processing Lab 1 Nov 29, 2021
「PyTorch Implementation of AnimeGANv2」を用いて、生成した顔画像を元の画像に上書きするデモ

AnimeGANv2-Face-Overlay-Demo PyTorch Implementation of AnimeGANv2を用いて、生成した顔画像を元の画像に上書きするデモです。

KazuhitoTakahashi 21 Oct 18, 2022
Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN)

Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN) This code implements the skeleton-based action segmentation MS-GCN model from Autom

Benjamin Filtjens 8 Nov 29, 2022
SMCA replication There are no extra compiled components in SMCA DETR and package dependencies are minimal

Usage There are no extra compiled components in SMCA DETR and package dependencies are minimal, so the code is very simple to use. We provide instruct

22 May 06, 2022
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

BiDR Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval. Requirements torch==

Microsoft 11 Oct 20, 2022
U-Net for GBM

My Final Year Project(FYP) In National University of Singapore(NUS) You need Pytorch(stable 1.9.1) Both cuda version and cpu version are OK File Str

PinkR1ver 1 Oct 27, 2021