<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kawamura-etal-2020-neural">
    <titleInfo>
        <title>Neural text normalization leveraging similarities of strings and sounds</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Riku</namePart>
        <namePart type="family">Kawamura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tatsuya</namePart>
        <namePart type="family">Aoki</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hidetaka</namePart>
        <namePart type="family">Kamigaito</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hiroya</namePart>
        <namePart type="family">Takamura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Manabu</namePart>
        <namePart type="family">Okumura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.</abstract>
    <identifier type="citekey">kawamura-etal-2020-neural</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.192</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.192</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>2126</start>
            <end>2131</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural text normalization leveraging similarities of strings and sounds
%A Kawamura, Riku
%A Aoki, Tatsuya
%A Kamigaito, Hidetaka
%A Takamura, Hiroya
%A Okumura, Manabu
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F kawamura-etal-2020-neural
%X We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.
%R 10.18653/v1/2020.coling-main.192
%U https://aclanthology.org/2020.coling-main.192
%U https://doi.org/10.18653/v1/2020.coling-main.192
%P 2126-2131

Markdown (Informal)

[Neural text normalization leveraging similarities of strings and sounds](https://aclanthology.org/2020.coling-main.192) (Kawamura et al., COLING 2020)

• Neural text normalization leveraging similarities of strings and sounds (Kawamura et al., COLING 2020)

ACL

• Riku Kawamura, Tatsuya Aoki, Hidetaka Kamigaito, Hiroya Takamura, and Manabu Okumura. 2020. Neural text normalization leveraging similarities of strings and sounds. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2126–2131, Barcelona, Spain (Online). International Committee on Computational Linguistics.