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        <title>Measuring the Compositionality of Noun-Noun Compounds over Time</title>
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    <name type="personal">
        <namePart type="given">Prajit</namePart>
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    <name type="personal">
        <namePart type="given">Lonneke</namePart>
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            <title>Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change</title>
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            <namePart type="given">Nina</namePart>
            <namePart type="family">Tahmasebi</namePart>
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            <namePart type="given">Lars</namePart>
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            <namePart type="given">Adam</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>We present work in progress on the temporal progression of compositionality in noun-noun compounds. Previous work has proposed computational methods for determining the compositionality of compounds. These methods try to automatically determine how transparent the meaning of the compound as a whole is with respect to the meaning of its parts. We hypothesize that such a property might change over time. We use the time-stamped Google Books corpus for our diachronic investigations, and first examine whether the vector-based semantic spaces extracted from this corpus are able to predict compositionality ratings, despite their inherent limitations. We find that using temporal information helps predicting the ratings, although correlation with the ratings is lower than reported for other corpora. Finally, we show changes in compositionality over time for a selection of compounds.</abstract>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Measuring the Compositionality of Noun-Noun Compounds over Time
%A Dhar, Prajit
%A Pagel, Janis
%A van der Plas, Lonneke
%Y Tahmasebi, Nina
%Y Borin, Lars
%Y Jatowt, Adam
%Y Xu, Yang
%S Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F dhar-etal-2019-measuring
%X We present work in progress on the temporal progression of compositionality in noun-noun compounds. Previous work has proposed computational methods for determining the compositionality of compounds. These methods try to automatically determine how transparent the meaning of the compound as a whole is with respect to the meaning of its parts. We hypothesize that such a property might change over time. We use the time-stamped Google Books corpus for our diachronic investigations, and first examine whether the vector-based semantic spaces extracted from this corpus are able to predict compositionality ratings, despite their inherent limitations. We find that using temporal information helps predicting the ratings, although correlation with the ratings is lower than reported for other corpora. Finally, we show changes in compositionality over time for a selection of compounds.
%R 10.18653/v1/W19-4729
%U https://aclanthology.org/W19-4729
%U https://doi.org/10.18653/v1/W19-4729
%P 234-239

Markdown (Informal)

[Measuring the Compositionality of Noun-Noun Compounds over Time](https://aclanthology.org/W19-4729) (Dhar et al., LChange 2019)

• Measuring the Compositionality of Noun-Noun Compounds over Time (Dhar et al., LChange 2019)

ACL

• Prajit Dhar, Janis Pagel, and Lonneke van der Plas. 2019. Measuring the Compositionality of Noun-Noun Compounds over Time. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, pages 234–239, Florence, Italy. Association for Computational Linguistics.