<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="raji-de-melo-2021-guilt">
    <titleInfo>
        <title>Guilt by Association: Emotion Intensities in Lexical Representations</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shahab</namePart>
        <namePart type="family">Raji</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gerard</namePart>
        <namePart type="family">de Melo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Scott</namePart>
            <namePart type="given">Wen-tau</namePart>
            <namePart type="family">Yih</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.</abstract>
    <identifier type="citekey">raji-de-melo-2021-guilt</identifier>
    <identifier type="doi">10.18653/v1/2021.emnlp-main.781</identifier>
    <location>
        <url>https://aclanthology.org/2021.emnlp-main.781</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>9911</start>
            <end>9917</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Guilt by Association: Emotion Intensities in Lexical Representations
%A Raji, Shahab
%A de Melo, Gerard
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F raji-de-melo-2021-guilt
%X What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.
%R 10.18653/v1/2021.emnlp-main.781
%U https://aclanthology.org/2021.emnlp-main.781
%U https://doi.org/10.18653/v1/2021.emnlp-main.781
%P 9911-9917

Markdown (Informal)

[Guilt by Association: Emotion Intensities in Lexical Representations](https://aclanthology.org/2021.emnlp-main.781) (Raji & de Melo, EMNLP 2021)

• Guilt by Association: Emotion Intensities in Lexical Representations (Raji & de Melo, EMNLP 2021)

ACL

• Shahab Raji and Gerard de Melo. 2021. Guilt by Association: Emotion Intensities in Lexical Representations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9911–9917, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.