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    <titleInfo>
        <title>Visually Grounded Interpretation of Noun-Noun Compounds in English</title>
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        <namePart type="given">Inga</namePart>
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            <title>Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics</title>
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            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
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            <namePart type="given">Nora</namePart>
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            <namePart type="given">Cassandra</namePart>
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        <name type="personal">
            <namePart type="given">Enrico</namePart>
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    <abstract>Noun-noun compounds (NNCs) occur frequently in the English language. Accurate NNC interpretation, i.e. determining the implicit relationship between the constituents of a NNC, is crucial for the advancement of many natural language processing tasks. Until now, computational NNC interpretation has been limited to approaches involving linguistic representations only. However, much research suggests that grounding linguistic representations in vision or other modalities can increase performance on this and other tasks. Our work is a novel comparison of linguistic and visuo-linguistic representations for the task of NNC interpretation. We frame NNC interpretation as a relation classification task, evaluating on a large, relationally-annotated NNC dataset. We combine distributional word vectors with image vectors to investigate how visual information can help improve NNC interpretation systems. We find that adding visual vectors increases classification performance on our dataset in many cases.</abstract>
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    <identifier type="doi">10.18653/v1/2022.cmcl-1.3</identifier>
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%0 Conference Proceedings
%T Visually Grounded Interpretation of Noun-Noun Compounds in English
%A Lang, Inga
%A Plas, Lonneke
%A Nissim, Malvina
%A Gatt, Albert
%Y Chersoni, Emmanuele
%Y Hollenstein, Nora
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lang-etal-2022-visually
%X Noun-noun compounds (NNCs) occur frequently in the English language. Accurate NNC interpretation, i.e. determining the implicit relationship between the constituents of a NNC, is crucial for the advancement of many natural language processing tasks. Until now, computational NNC interpretation has been limited to approaches involving linguistic representations only. However, much research suggests that grounding linguistic representations in vision or other modalities can increase performance on this and other tasks. Our work is a novel comparison of linguistic and visuo-linguistic representations for the task of NNC interpretation. We frame NNC interpretation as a relation classification task, evaluating on a large, relationally-annotated NNC dataset. We combine distributional word vectors with image vectors to investigate how visual information can help improve NNC interpretation systems. We find that adding visual vectors increases classification performance on our dataset in many cases.
%R 10.18653/v1/2022.cmcl-1.3
%U https://aclanthology.org/2022.cmcl-1.3
%U https://doi.org/10.18653/v1/2022.cmcl-1.3
%P 23-35

Markdown (Informal)

[Visually Grounded Interpretation of Noun-Noun Compounds in English](https://aclanthology.org/2022.cmcl-1.3) (Lang et al., CMCL 2022)

• Visually Grounded Interpretation of Noun-Noun Compounds in English (Lang et al., CMCL 2022)

ACL

• Inga Lang, Lonneke Plas, Malvina Nissim, and Albert Gatt. 2022. Visually Grounded Interpretation of Noun-Noun Compounds in English. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 23–35, Dublin, Ireland. Association for Computational Linguistics.