@inproceedings{sims-etal-2019-literary,
title = "Literary Event Detection",
author = "Sims, Matthew and
Park, Jong Ho and
Bamman, David",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1353",
doi = "10.18653/v1/P19-1353",
pages = "3623--3634",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Literary Event Detection
%A Sims, Matthew
%A Park, Jong Ho
%A Bamman, David
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F sims-etal-2019-literary
%X 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.
%R 10.18653/v1/P19-1353
%U https://aclanthology.org/P19-1353
%U https://doi.org/10.18653/v1/P19-1353
%P 3623-3634
Markdown (Informal)
[Literary Event Detection](https://aclanthology.org/P19-1353) (Sims et al., ACL 2019)
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.