Attributing a particular property to a person by naming another person, who is typically wellknown for the respective property, is called a Vossian Antonomasia (VA). This subtpye of metonymy, which overlaps with metaphor, has a specific syntax and is especially frequent in journalistic texts. While identifying Vossian Antonomasia is of particular interest in the study of stylistics, it is also a source of errors in relation and fact extraction as an explicitly mentioned entity occurs only metaphorically and should not be associated with respective contexts. Despite rather simple syntactic variations, the automatic extraction of VA was never addressed as yet since it requires a deeper semantic understanding of mentioned entities and underlying relations. In this paper, we propose a first method for the extraction of VAs that works completely automatically. Our approaches use named entity recognition, distant supervision based on Wikidata, and a bi-directional LSTM for postprocessing. The evaluation on 1.8 million articles of the New York Times corpus shows that our approach significantly outperforms the only existing semi-automatic approach for VA identification by more than 30 percentage points in precision.

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Vossian Antonomasia is a prolific stylistic device, in use since antiquity. It can compress the introduction or description of a person or another named entity into a terse, poignant formulation and can best be explained by an example: When Norwegian world champion Magnus Carlsen is described as "the Mozart of chess", it is Vossian Antonomasia we are dealing with. The pattern is simple: A source (Mozart) is used to describe a target (Magnus Carlsen), the transfer of meaning is reached via a modifier ("of chess"). This phenomenon has been discussed before (as 'metaphorical antonomasia' or, with special focus on the source object, as 'paragons'), but no corpusbased approach has been undertaken as yet to explore its breadth and variety. We are looking into a full-text newspaper corpus (The New York Times, 1987-2007 and describe a new method for the automatic extraction of Vossian Antonomasia based on Wikidata entities. Our analysis offers new insights into the occurrence of popular paragons and their distribution.

First and foremost, the editor of this volume would like to thank the European Science Foundation for making possible both the original working group along with its meetings, and this open access publication. The NeDiMAH network continues to be a point of reference for scholars who are exploring not just how to use digital methods in the humanities and what it means to do this, but also what is at stake in the digital turn for our diverse and yet interconnected disciplines. It would be remiss not to also thank the participants in the NeDiMAH events: their contributions to that early discussion are woven into the fabric of this volume and the issues it pursues. In particular, I would like to thank the Zadar meeting group:

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