# NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification

arXiv: https://arxiv.org/abs/2007.01030 · HTML: https://mlsanity.com/paper/2007.01030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain](https://mlsanity.com/paper/2112.08754/) (2021)
- [Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting](https://mlsanity.com/paper/2109.08597/) (2021)

## Abstract (arXiv)

Natural language processing has huge potential in the medical domain which recently led to a lot of research in this field. However, a prerequisite of secure processing of medical documents, e.g., patient notes and clinical trials, is the proper de-identification of privacy-sensitive information. In this paper, we describe our NLNDE system, with which we participated in the MEDDOCAN competition, the medical document anonymization task of IberLEF 2019. We address the task of detecting and classifying protected health information from Spanish data as a sequence-labeling problem and investigate different embedding methods for our neural network. Despite dealing in a non-standard language and domain setting, the NLNDE system achieves promising results in the competition.