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    <titleInfo>
        <title>Measure words are measurably different from sortal classifiers</title>
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        <namePart type="given">Yamei</namePart>
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        <dateIssued>2023-03</dateIssued>
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            <title>Proceedings of the Seventh International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023)</title>
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        <name type="personal">
            <namePart type="given">Owen</namePart>
            <namePart type="family">Rambow</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">François</namePart>
            <namePart type="family">Lareau</namePart>
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    <abstract>Nominal classifiers categorize nouns based on salient semantic properties. Past studies have long debated whether sortal classifiers (related to intrinsic semantic noun features) and mensural classifiers (related to quantity) should be considered as the same grammatical category. Suggested diagnostic tests rely on functional and distributional criteria, typically evaluated in terms of isolated example sentences obtained through elicitation. This paper offers a systematic re-evaluation of this long-standing question: using 981,076 nominal phrases from a 489 MB dependency-parsed word corpus, corresponding extracted contextual word embeddings from a Chinese BERT model, and information-theoretic measures of mutual information, we show that mensural classifiers can be distributionally and functionally distinguished from sortal classifiers justifying the existence of distinct syntactic categories for mensural and sortal classifiers. Our study also entails broader implications for the typological study of classifier systems.</abstract>
    <identifier type="citekey">wang-walther-2023-measure</identifier>
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        <url>https://aclanthology.org/2023.depling-1.8</url>
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        <date>2023-03</date>
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            <start>81</start>
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%0 Conference Proceedings
%T Measure words are measurably different from sortal classifiers
%A Wang, Yamei
%A Walther, Géraldine
%Y Rambow, Owen
%Y Lareau, François
%S Proceedings of the Seventh International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023)
%D 2023
%8 March
%I Association for Computational Linguistics
%C Washington, D.C.
%F wang-walther-2023-measure
%X Nominal classifiers categorize nouns based on salient semantic properties. Past studies have long debated whether sortal classifiers (related to intrinsic semantic noun features) and mensural classifiers (related to quantity) should be considered as the same grammatical category. Suggested diagnostic tests rely on functional and distributional criteria, typically evaluated in terms of isolated example sentences obtained through elicitation. This paper offers a systematic re-evaluation of this long-standing question: using 981,076 nominal phrases from a 489 MB dependency-parsed word corpus, corresponding extracted contextual word embeddings from a Chinese BERT model, and information-theoretic measures of mutual information, we show that mensural classifiers can be distributionally and functionally distinguished from sortal classifiers justifying the existence of distinct syntactic categories for mensural and sortal classifiers. Our study also entails broader implications for the typological study of classifier systems.
%U https://aclanthology.org/2023.depling-1.8
%P 81-90

Markdown (Informal)

[Measure words are measurably different from sortal classifiers](https://aclanthology.org/2023.depling-1.8) (Wang & Walther, DepLing-SyntaxFest 2023)

• Measure words are measurably different from sortal classifiers (Wang & Walther, DepLing-SyntaxFest 2023)

ACL

• Yamei Wang and Géraldine Walther. 2023. Measure words are measurably different from sortal classifiers. In Proceedings of the Seventh International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023), pages 81–90, Washington, D.C.. Association for Computational Linguistics.