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        <title>A Systematic Comparison of English Noun Compound Representations</title>
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            <namePart type="given">Agata</namePart>
            <namePart type="family">Savary</namePart>
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        <name type="personal">
            <namePart type="given">Carla</namePart>
            <namePart type="given">Parra</namePart>
            <namePart type="family">Escartín</namePart>
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            <namePart type="given">Francis</namePart>
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        <name type="personal">
            <namePart type="given">Jelena</namePart>
            <namePart type="family">Mitrović</namePart>
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            <namePart type="given">Verginica</namePart>
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    <abstract>Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus. To that end, composition functions approximate the distributional representation of a noun compound by combining its constituent distributional vectors. In the more general case, phrase embeddings have been trained by minimizing the distance between the vectors representing paraphrases. We compare various types of noun compound representations, including distributional, compositional, and paraphrase-based representations, through a series of tasks and analyses, and with an extensive number of underlying word embeddings. We find that indeed, in most cases, composition functions produce higher quality representations than distributional ones, and they improve with computational power. No single function performs best in all scenarios, suggesting that a joint training objective may produce improved representations.</abstract>
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        <date>2019-08</date>
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            <start>92</start>
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%0 Conference Proceedings
%T A Systematic Comparison of English Noun Compound Representations
%A Shwartz, Vered
%Y Savary, Agata
%Y Escartín, Carla Parra
%Y Bond, Francis
%Y Mitrović, Jelena
%Y Mititelu, Verginica Barbu
%S Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F shwartz-2019-systematic
%X Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus. To that end, composition functions approximate the distributional representation of a noun compound by combining its constituent distributional vectors. In the more general case, phrase embeddings have been trained by minimizing the distance between the vectors representing paraphrases. We compare various types of noun compound representations, including distributional, compositional, and paraphrase-based representations, through a series of tasks and analyses, and with an extensive number of underlying word embeddings. We find that indeed, in most cases, composition functions produce higher quality representations than distributional ones, and they improve with computational power. No single function performs best in all scenarios, suggesting that a joint training objective may produce improved representations.
%R 10.18653/v1/W19-5111
%U https://aclanthology.org/W19-5111
%U https://doi.org/10.18653/v1/W19-5111
%P 92-103

Markdown (Informal)

[A Systematic Comparison of English Noun Compound Representations](https://aclanthology.org/W19-5111) (Shwartz, MWE 2019)

• A Systematic Comparison of English Noun Compound Representations (Shwartz, MWE 2019)

ACL

• Vered Shwartz. 2019. A Systematic Comparison of English Noun Compound Representations. In Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019), pages 92–103, Florence, Italy. Association for Computational Linguistics.