Automation on Legal Court Cases Review

This project is to automate the case review on legal case documents and find the most critical cases using network analysis.

Short write-up

Affiliation: Institute for Social and Economic Research and Policy, Columbia University

Project Information:

Keywords: Automation, PDF parse, String Extraction, Network Analysis

Software:

• Python : pdfminer, LexNLP, nltk sklearn
• R: igraph

Scope:

1. Parse court documents, extract citations from raw text.
2. Build citation network, identify important cases in the network.
3. Extract judge's opinion text and meta information including opinion author, court, decision.
4. Model training to predict court decision based on opinion text.

Polit Study on 159 Legal Court Documents (in pilot_159 folder)

1. Process PDF documents using Python

2. Data: Parse PDF documents via Python

These datasets are NOT included in this public repository for intellectual property and privacy concern

3. Analyze and Visualize using R

4. Visulization Chart Sample

Citation Frequency

Citation Network

Network Visulization and Predictive Modeling on 854 Legal Court Cases (in Extraction_Modelling folder)

1. Extract opinion and meta information from raw text data

2. Datasets

These datasets are NOT included in this public repository for intellectual property and privacy concern

3. Visulization using R

Interactive Graph

Play with Interactive Graph

Full Citation Network (all cases and cited cases)

Citation Between Available Cases

4. Predictive Modeling using Python

Visulization of the Bi-gram (words) with the strongest coefficient