<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sui-etal-2023-mrs">
    <titleInfo>
        <title>Mrs. Dalloway Said She Would Segment the Chapters Herself</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Peiqi</namePart>
        <namePart type="family">Sui</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lin</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sil</namePart>
        <namePart type="family">Hamilton</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Thorsten</namePart>
        <namePart type="family">Ries</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kelvin</namePart>
        <namePart type="family">Wong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stephen</namePart>
        <namePart type="family">Wong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Workshop on Narrative Understanding</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nader</namePart>
            <namePart type="family">Akoury</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Clark</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Iyyer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Snigdha</namePart>
            <namePart type="family">Chaturvedi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Faeze</namePart>
            <namePart type="family">Brahman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Khyathi</namePart>
            <namePart type="family">Chandu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>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.</abstract>
    <identifier type="citekey">sui-etal-2023-mrs</identifier>
    <identifier type="doi">10.18653/v1/2023.wnu-1.15</identifier>
    <location>
        <url>https://aclanthology.org/2023.wnu-1.15/</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>92</start>
            <end>105</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Mrs. Dalloway Said She Would Segment the Chapters Herself
%A Sui, Peiqi
%A Wang, Lin
%A Hamilton, Sil
%A Ries, Thorsten
%A Wong, Kelvin
%A Wong, Stephen
%Y Akoury, Nader
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brahman, Faeze
%Y Chandu, Khyathi
%S Proceedings of the 5th Workshop on Narrative Understanding
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sui-etal-2023-mrs
%X 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.
%R 10.18653/v1/2023.wnu-1.15
%U https://aclanthology.org/2023.wnu-1.15/
%U https://doi.org/10.18653/v1/2023.wnu-1.15
%P 92-105

Markdown (Informal)

[Mrs. Dalloway Said She Would Segment the Chapters Herself](https://aclanthology.org/2023.wnu-1.15/) (Sui et al., WNU 2023)

• Mrs. Dalloway Said She Would Segment the Chapters Herself (Sui et al., WNU 2023)

ACL

• Peiqi Sui, Lin Wang, Sil Hamilton, Thorsten Ries, Kelvin Wong, and Stephen Wong. 2023. Mrs. Dalloway Said She Would Segment the Chapters Herself. In Proceedings of the 5th Workshop on Narrative Understanding, pages 92–105, Toronto, Canada. Association for Computational Linguistics.