Literary Event Detection

Matthew Sims, Jong Ho Park, David Bamman


Abstract
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.
Anthology ID:
P19-1353
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3623–3634
Language:
URL:
https://aclanthology.org/P19-1353
DOI:
10.18653/v1/P19-1353
Bibkey:
Cite (ACL):
Matthew Sims, Jong Ho Park, and David Bamman. 2019. Literary Event Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3623–3634, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Literary Event Detection (Sims et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1353.pdf
Video:
 https://aclanthology.org/P19-1353.mp4
Code
 dbamman/litbank +  additional community code