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Modern performance on several natural language processing (NLP) tasks hasbeen enhanced thanks to the Transformer-based pre-trained language model BERT.We employ this concept to investigate a local publication database. Researchpapers are encoded and clustered to form a landscape view of the scientifictopics, in which research is active. Authors working on similar topics can beidentified by calculating the similarity between their papers. Based on this,we define a similarity metric between authors. Additionally we introduce theconcept of self-similarity to indicate the topical variety of authors.

Mapping Researcher Activity based on Publication Data by means of Transformers