Literary Event Detection

ACL 2019  ·  Matthew Sims, Jong Ho Park, David Bamman ·

In this work we present a new dataset of literary events{---}events that are depicted as taking place within the imagined space of a novel. While previous work has focused on event detection in the domain of contemporary news, literature poses a number of complications for existing systems, including complex narration, the depiction of a broad array of mental states, and a strong emphasis on figurative language. We outline the annotation decisions of this new dataset and compare several models for predicting events; the best performing model, a bidirectional LSTM with BERT token representations, achieves an F1 score of 73.9. We then apply this model to a corpus of novels split across two dimensions{---}prestige and popularity{---}and demonstrate that there are statistically significant differences in the distribution of events for prestige.

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