An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry
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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.
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
8 of 21 references.
References · 8
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- 2014Distributed Representations of Sentences and Documentscited by 678
- 2017Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Featurescited by 104
- 2018Simple Unsupervised Keyphrase Extraction using Sentence Embeddingscited by 62
- 2019A Review of Keyphrase Extractioncited by 33
- 2022PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extractioncited by 19
- 2023From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Surveycited by 14
- 2023The Recent Advances in Automatic Term Extraction: A surveycited by 4
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Citing papers per year: 2026: 2.
2 papers.