Living Machines: A study of atypical animacy Mariona Coll Ardanuy Federico Nanni The Alan Turing Institute The Alan Turing Institute Queen Mary University of London
[email protected] [email protected] Kaspar Beelen Kasra Hosseini The Alan Turing Institute The Alan Turing Institute Queen Mary University of London
[email protected] [email protected] Ruth Ahnert Jon Lawrence Queen Mary University of London University of Exeter
[email protected] [email protected] Katherine McDonough Giorgia Tolfo The Alan Turing Institute The British Library Queen Mary University of London
[email protected] [email protected] Daniel CS Wilson Barbara McGillivray The Alan Turing Institute The Alan Turing Institute Queen Mary University of London University of Cambridge
[email protected] [email protected] Abstract This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource.