# An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry

arXiv: https://arxiv.org/abs/2407.14085 · HTML: https://mlsanity.com/paper/2407.14085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 19 Jul 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2210.05245/) (2022)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [The Recent Advances in Automatic Term Extraction: A survey](https://mlsanity.com/paper/2301.06767/) (2023)

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

- [Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings](https://mlsanity.com/paper/2605.21029/) (2026)
- [Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics](https://mlsanity.com/paper/2606.03864/) (2026)

## Abstract (arXiv)

The task of textitkeyword extraction is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document classification. While recent methods have proven to be quite effective in the extraction of keywords, the identification of textitclass-specific keywords, or only those pertaining to a predefined class, remains challenging. In this work, we propose an improved method for class-specific keyword extraction, which builds upon the popular KeyBERT library to identify only keywords related to a class described by textitseed keywords. We test this method using a dataset of German business registry entries, where the goal is to classify each business according to an economic sector. Our results reveal that our method greatly improves upon previous approaches, setting a new standard for textitclass-specific keyword extraction.