# Classification of Contract-Amendment Relationships

arXiv: https://arxiv.org/abs/2106.14619 · HTML: https://mlsanity.com/paper/2106.14619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts](https://mlsanity.com/paper/2012.01942/) (2020)

## Abstract (arXiv)

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.