Classification of Contract-Amendment Relationships
This work addresses the need for efficient contract life-cycle management for legal practitioners, though it is incremental in applying existing ML/NLP methods to a specific domain.
The paper tackled the problem of automatically detecting amendment relationships between contract documents using machine learning and NLP, achieving a 91% F1-score, which outperformed a heuristic baseline by 23%.
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.