# Towards De-identification of Legal Texts

arXiv: https://arxiv.org/abs/1910.03739 · HTML: https://mlsanity.com/paper/1910.03739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse](https://mlsanity.com/paper/2303.13451/) (2023)

## Abstract (arXiv)

In many countries, personal information that can be published or shared between organizations is regulated and, therefore, documents must undergo a process of de-identification to eliminate or obfuscate confidential data. Our work focuses on the de-identification of legal texts, where the goal is to hide the names of the actors involved in a lawsuit without losing the sense of the story. We present a first evaluation on our corpus of NLP tools in tasks such as segmentation, tokenization and recognition of named entities, and we analyze several evaluation measures for our de-identification task. Results are meager: 84% of the documents have at least one name not covered by NER tools, something that might lead to the re-identification of involved names. We conclude that tools must be strongly adapted for processing texts of this particular domain.