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    <titleInfo>
        <title>Quotation Detection and Classification with a Corpus-Agnostic Model</title>
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    <name type="personal">
        <namePart type="given">Sean</namePart>
        <namePart type="family">Papay</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task. However, state-of-the-art models have been developed on the basis of specific corpora and incorpo- rate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation. In the spirit of task-agnostic modeling, we present a corpus-agnostic neural model for quotation detection and evaluate it on three corpora that vary in language, text genre, and structural assumptions. The model (a) approaches the state-of-the-art on the corpora when using established feature sets and (b) shows reasonable performance even when us- ing solely word forms, which makes it applicable for non-standard (i.e., historical) corpora.</abstract>
    <identifier type="citekey">papay-pado-2019-quotation</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_103</identifier>
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        <url>https://aclanthology.org/R19-1103</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Quotation Detection and Classification with a Corpus-Agnostic Model
%A Papay, Sean
%A Padó, Sebastian
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F papay-pado-2019-quotation
%X The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task. However, state-of-the-art models have been developed on the basis of specific corpora and incorpo- rate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation. In the spirit of task-agnostic modeling, we present a corpus-agnostic neural model for quotation detection and evaluate it on three corpora that vary in language, text genre, and structural assumptions. The model (a) approaches the state-of-the-art on the corpora when using established feature sets and (b) shows reasonable performance even when us- ing solely word forms, which makes it applicable for non-standard (i.e., historical) corpora.
%R 10.26615/978-954-452-056-4_103
%U https://aclanthology.org/R19-1103
%U https://doi.org/10.26615/978-954-452-056-4_103
%P 888-894

Markdown (Informal)

[Quotation Detection and Classification with a Corpus-Agnostic Model](https://aclanthology.org/R19-1103) (Papay & Padó, RANLP 2019)

• Quotation Detection and Classification with a Corpus-Agnostic Model (Papay & Padó, RANLP 2019)

ACL

• Sean Papay and Sebastian Padó. 2019. Quotation Detection and Classification with a Corpus-Agnostic Model. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 888–894, Varna, Bulgaria. INCOMA Ltd..