TY - GEN
T1 - Mrs. Dalloway Said She Would Segment the Chapters Herself
AU - Sui, Peiqi
AU - Wang, Lin
AU - Hamilton, Sil
AU - Ries, Thorsten
AU - Wong, Kelvin
AU - Wong, Stephen T.
N1 - Funding Information:
We sincerely thank Fangyuan Xu, the workshop organizers, and the anonymous reviewers for their generous time, attention, and helpful feedback on this paper. Peiqi Sui, Lin Wang, Kelvin Wong, and Stephen T. Wong were supported by T. T. & W. F. Chao Foundation and the John S Dunn Research Foundation.
Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - This paper proposes a sentiment-centric pipeline to perform unsupervised plot extraction on non-linear novels like Virginia Woolf’s Mrs. Dalloway, a novel widely considered to be “plotless.” Combining transformer-based sentiment analysis models with statistical testing, we model sentiment’s rate-of-change and correspondingly segment the novel into emotionally self-contained units qualitatively evaluated to be meaningful surrogate pseudo-chapters. We validate our findings by evaluating our pipeline as a fully unsupervised text segmentation model, achieving a F-1 score of 0.643 (regional) and 0.214 (exact) in chapter break prediction on a validation set of linear novels with existing chapter structures. In addition, we observe notable differences between the distributions of predicted chapter lengths in linear and non-linear fictional narratives, with the latter exhibiting significantly greater variability. Our results hold significance for narrative researchers appraising methods for extracting plots from non-linear novels.
AB - This paper proposes a sentiment-centric pipeline to perform unsupervised plot extraction on non-linear novels like Virginia Woolf’s Mrs. Dalloway, a novel widely considered to be “plotless.” Combining transformer-based sentiment analysis models with statistical testing, we model sentiment’s rate-of-change and correspondingly segment the novel into emotionally self-contained units qualitatively evaluated to be meaningful surrogate pseudo-chapters. We validate our findings by evaluating our pipeline as a fully unsupervised text segmentation model, achieving a F-1 score of 0.643 (regional) and 0.214 (exact) in chapter break prediction on a validation set of linear novels with existing chapter structures. In addition, we observe notable differences between the distributions of predicted chapter lengths in linear and non-linear fictional narratives, with the latter exhibiting significantly greater variability. Our results hold significance for narrative researchers appraising methods for extracting plots from non-linear novels.
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M3 - Conference contribution
AN - SCOPUS:85175655252
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 92
EP - 105
BT - 5th Workshop on Narrative Understanding, WNU 2023 - Proceedings of the Workshop
A2 - Akoury, Nader
A2 - Clark, Elizabeth
A2 - Iyyer, Mohit
A2 - Chaturvedi, Snigdha
A2 - Brahman, Faeze
A2 - Chandu, Khyathi Raghavi
PB - Association for Computational Linguistics (ACL)
T2 - 5th Workshop on Narrative Understanding, WNU 2023
Y2 - 14 July 2023
ER -