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.

Project Information:

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


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


  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

Ipython Notebook Description
1.Extraction by LexNLP.ipynb Extract meta inforation use LexNLP package.
2.Layer Analysis on Sigle File. ipynb Use pdfminer to extract the raw text and the paragraph segamentation in the PDF document.
3.Patent Position by Layer.ipynb Identify the position of patent number in extracted layers from PDF.
4.Opinion and Author by Layer.ipynb Extract opinion text, author, decisions from the layers list.
5.Wrap up to Meta Data.ipynb Store extracted meta data to .json or .csv
6.Visualize citation frequency.ipynb Bar plot of the citation frequencies

2. Data: Parse PDF documents via Python

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

pdf2text159.json A dictionary of 3 list: file_name, raw_text, layers.
cite_edge159.csv Edge list of citation network
cite_node159.csv Meta information of each case: case_number, court, dates
reference_extract.csv cited cases in a list for every case, untidy format for analysis
citation159.csv file citation pair, tidy format for calculation
regulation159.csv file regulation pair, tidy format for calculation

3. Analyze and Visualize using R

Calculate Citation Frequency.Rmd Analyze reference_extract.csv
Citation Network.Rmd Analyze cite_edge159

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

.ipynb notebook Description
Full Dataset Merge.ipynb Merge the 854 cases dataset
Edge and Node List.ipynb Create edge and node list
Full Extractions.ipynb Extract author, judge panel, opinion text
Clean Opinion Text.ipynb Remove references and special characters in opinion text

2. Datasets

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

Dataset Description
amy_cases.json large dictionary {file name: raw text} for 854 cases, from Lilian's PDF parsing
full_name_text.json convert amy_cases.json key value pair to two list: file_name, raw_text
cite_edge.csv edge list of citation
cite_node.csv node list contains case_code, case_name, court_from, court_type
extraction854.csv full extractions include case_code, case_name, court_from, court_type, result, author, judge_panel
decision_text.json json file include author, decision(result of the case), opinion (opinion text), cleaned_text (cleaned opinion text)
cleaned_text.csv csv file contains allt the cleaned text
predict_data.csv cleaned dataset for NLP modeling predict court decision

3. Visulization using R

R markdown file
Full Network Graph.Rmd draw the full citation network
Citation Betwwen Nodes.Rmd draw citation between all the available cases
Clean Data For Predictive Modelling.rmd clean text data for predictive modeling

Full Citation Network (all cases and cited cases)


Citation Between Available Cases


4. Predictive Modeling using Python

ipynb notebook
NLP Predictive Modeling.ipynb Try different preprocessing, and build a logistic regression to predict court decision.

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