# ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

arXiv: https://arxiv.org/abs/2112.06724 · HTML: https://mlsanity.com/paper/2112.06724/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018)
- [DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool](https://mlsanity.com/paper/2010.04141/) (2020)
- [A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events](https://mlsanity.com/paper/2004.03283/) (2020)
- [Named Entity Recognition with Extremely Limited Data](https://mlsanity.com/paper/1806.04411/) (2018)

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

- [Automated Collection of Evaluation Dataset for Semantic Search in Low-Resource Domain Language](https://mlsanity.com/paper/2412.10008/) (2024)

## Abstract (arXiv)

Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its common and viable use in many use cases, NER is barely applicable in domains where general categories are suboptimal, such as engineering or medicine. To facilitate NER of domain-specific types, we propose ANEA, an automated (named) entity annotator to assist human annotators in creating domain-specific NER corpora for German text collections when given a set of domain-specific texts. In our evaluation, we find that ANEA automatically identifies terms that best represent the texts' content, identifies groups of coherent terms, and extracts and assigns descriptive labels to these groups, i.e., annotates text datasets into the domain (named) entities.