  The task of $\textit{keyword extraction}$ 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
$\textit{class-specific}$ 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
$\textbf{KeyBERT}$ library to identify only keywords related to a class
described by $\textit{seed keywords}$. 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
$\textit{class-specific}$ keyword extraction.
