# A Case Study on Pros and Cons of Regular Expression Detection and Dependency Parsing for Negation Extraction from German Medical Documents. Technical Report

arXiv: https://arxiv.org/abs/2105.09702 · HTML: https://mlsanity.com/paper/2105.09702/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 3 of 3)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [NegBio: a high-performance tool for negation and uncertainty detection in radiology reports](https://mlsanity.com/paper/1712.05898/) (2017)
- [Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification](https://mlsanity.com/paper/2003.00353/) (2020)

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

- [Semgrex and Ssurgeon, Searching and Manipulating Dependency Graphs](https://mlsanity.com/paper/2404.16250/) (2024)

## Abstract (arXiv)

We describe our work on information extraction in medical documents written in German, especially detecting negations using an architecture based on the UIMA pipeline. Based on our previous work on software modules to cover medical concepts like diagnoses, examinations, etc. we employ a version of the NegEx regular expression algorithm with a large set of triggers as a baseline. We show how a significantly smaller trigger set is sufficient to achieve similar results, in order to reduce adaptation times to new text types. We elaborate on the question whether dependency parsing (based on the Stanford CoreNLP model) is a good alternative and describe the potentials and shortcomings of both approaches.