Engineeering & IT Projects and Resources: AI Legal Document Analyzer

AI Legal Document Analyzer

1. Introduction

The AI Legal Document Analyzer is a project designed to streamline the review and analysis of legal documents. Using Natural Language Processing (NLP), this system can extract specific clauses, identify key legal terms, and generate summaries. This project is particularly beneficial for legal professionals, businesses, and organizations handling extensive legal paperwork.

2. Prerequisites

• Python: Install Python 3.x from the official Python website.
• Required Libraries:
- nltk: Install using pip install nltk
- spacy: Install using pip install spacy
- gensim: Install using pip install gensim
- pandas: Install using pip install pandas
- numpy: Install using pip install numpy
- PyPDF2 (optional): Install using pip install PyPDF2 (for handling PDF documents).
• Pre-trained NLP models: Spacy models (e.g., en_core_web_sm).
• Sample legal documents in text or PDF format.

3. Project Setup

1. Create a Project Directory:

- Name your project folder, e.g., `Legal_Document_Analyzer`.
- Inside this folder, create the Python script file (`legal_analyzer.py`).

2. Install Required Libraries:

Ensure NLTK, Spacy, Gensim, and other dependencies are installed using `pip`.

3. Download Spacy Language Model:

Run the command: python -m spacy download en_core_web_sm

4. Writing the Code

Below is an example code snippet for the AI Legal Document Analyzer:

import spacy
from gensim.summarization import summarize
from nltk.tokenize import sent_tokenize
import PyPDF2

# Load Spacy NLP model
nlp = spacy.load("en_core_web_sm")

# Function to read text from a PDF file
def extract_text_from_pdf(pdf_path):
    with open(pdf_path, 'rb') as pdf_file:
        reader = PyPDF2.PdfReader(pdf_file)
        text = ""
        for page in reader.pages:
            text += page.extract_text()
    return text

# Function to extract specific clauses
def extract_clauses(text, keywords):
    doc = nlp(text)
    sentences = sent_tokenize(text)
    clauses = [sentence for sentence in sentences if any(keyword in sentence for keyword in keywords)]
    return clauses

# Function to summarize text
def generate_summary(text):
    return summarize(text, word_count=100)

# Main function
def main():
    pdf_path = 'legal_document.pdf'
    keywords = ['termination', 'confidentiality', 'liability']

    # Extract text from PDF
    text = extract_text_from_pdf(pdf_path)

    # Extract clauses
  clauses = extract_clauses(text, keywords)
    print("Extracted Clauses:")
    for clause in clauses:
        print(f"- {clause}")

    # Generate summary
    summary = generate_summary(text)
    print("
Document Summary:")
    print(summary)

if __name__ == "__main__":
    main()

5. Key Components

• Text Extraction: Reads and processes text from documents (e.g., PDF, TXT).
• Clause Extraction: Identifies sentences or clauses containing specific keywords.
• Text Summarization: Provides a concise summary of the document using NLP techniques.

6. Testing

1. Ensure the legal document (`legal_document.pdf`) is available in the project directory.

2. Run the script:

python legal_analyzer.py

3. Verify the extracted clauses and generated summary.

7. Enhancements

• Advanced NLP Models: Use state-of-the-art models like BERT for clause extraction and summarization.
• Multi-Document Analysis: Enable processing of multiple documents at once.
• GUI or Web Interface: Provide a user-friendly interface for uploading and analyzing documents.

8. Troubleshooting

• Incorrect Clause Detection: Refine keywords or improve the NLP pipeline.
• PDF Parsing Issues: Ensure the PDF file is text-based and not an image scan.
• Library Compatibility: Verify library versions and dependencies.

9. Conclusion

The AI Legal Document Analyzer simplifies the review of legal documents by automating the extraction of critical information. This system demonstrates the potential of AI to enhance efficiency and accuracy in the legal domain.

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