Legal Clause Similarity Detection and Contract Section Matching using TF-IDF
Siva Sankar,
Setty Sowmya,
Shaik Sufiya,
Vellanki Jahnavi,
Yammani Bhavyonnathi
Legal contracts represent structured agreements that specify the rights, duties, and responsibilities
of participating parties such as individuals, businesses, or institutions. These contracts often contain
multiple clauses that define obligations, conditions, and responsibilities. Traditional manual contract
review requires significant effort and may lead to inconsistencies or oversight when analyzing large
legal documents, especially when contracts contain lengthy legal language and complex terminology.
This research proposes an automated approach for detecting similarity between legal clauses and
identifying matching contract sections using Natural Language Processing (NLP) techniques. The
system converts legal text into numerical representations using the Term Frequency–Inverse Document
Frequency (TF¬IDF) technique and applies a Logistic Regression model for classification.
The proposed system processes legal documents, extracts clauses, and calculates similarity scores
between different sections of contracts. This approach helps reduce manual effort, improves efficiency
in contract analysis, and assists legal professionals in identifying similar clauses quickly. Experimental
results demon¬strate that machine learning techniques can effectively support legal document analysis
and improve the accuracy of clause comparison.
