# Text clustering applied to data augmentation in legal contexts

arXiv: https://arxiv.org/abs/2404.08683 · HTML: https://mlsanity.com/paper/2404.08683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks](https://mlsanity.com/paper/1909.09586/) (2019)
- [Predicting the Law Area and Decisions of French Supreme Court Cases](https://mlsanity.com/paper/1708.01681/) (2017)
- [Analysing similarities between legal court documents using natural language processing approaches based on Transformers](https://mlsanity.com/paper/2204.07182/) (2022)

## Abstract (arXiv)

Data analysis and machine learning are of preeminent importance in the legal domain, especially in tasks like clustering and text classification. In this study, we harnessed the power of natural language processing tools to enhance datasets meticulously curated by experts. This process significantly improved the classification workflow for legal texts using machine learning techniques. We considered the Sustainable Development Goals (SDGs) data from the United Nations 2030 Agenda as a practical case study. Data augmentation clustering-based strategy led to remarkable enhancements in the accuracy and sensitivity metrics of classification models. For certain SDGs within the 2030 Agenda, we observed performance gains of over 15%. In some cases, the example base expanded by a noteworthy factor of 5. When dealing with unclassified legal texts, data augmentation strategies centered around clustering prove to be highly effective. They provide a valuable means to expand the existing knowledge base without the need for labor-intensive manual classification efforts.