Classification of Contract-Amendment Relationships

TL;DR

This paper presents a machine learning and NLP-based approach to automatically identify contract-amendment relationships in legal documents, significantly improving accuracy over heuristic methods.

Contribution

It introduces a novel ML and NLP method for classifying contract-amendment relationships using OCR and NER processed PDFs, with strong experimental results.

Findings

Achieved a 91% F1-score in classification accuracy.

Outperformed heuristic baseline by 23%.

Validated on a bilingual dataset of 1124 document pairs.

Abstract

In Contract Life-cycle Management (CLM), managing and tracking the master agreements and their associated amendments is essential, in order to be kept informed with different due dates and obligations. An automatic solution can facilitate the daily jobs and improve the efficiency of legal practitioners. In this paper, we propose an approach based on machine learning (ML) and Natural Language Processing (NLP) to detect the amendment relationship between two documents. The algorithm takes two PDF documents preprocessed by OCR (Optical Character Recognition) and NER (Named Entity Recognition) as input, and then it builds the features of each document pair and classifies the relationship. We experimented with different configurations on a dataset consisting of 1124 pairs of contract-amendment documents in English and French. The best result obtained a F1-score of 91%, which outperformed 23% compared to a heuristic-based baseline.