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        <title>How Large Are Lions? Inducing Distributions over Quantitative Attributes</title>
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    <name type="personal">
        <namePart type="given">Yanai</namePart>
        <namePart type="family">Elazar</namePart>
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    <name type="personal">
        <namePart type="given">Abhijit</namePart>
        <namePart type="family">Mahabal</namePart>
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    <name type="personal">
        <namePart type="given">Deepak</namePart>
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    <name type="personal">
        <namePart type="given">Tania</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as “Is a lion bigger than a wolf?”. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.</abstract>
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%0 Conference Proceedings
%T How Large Are Lions? Inducing Distributions over Quantitative Attributes
%A Elazar, Yanai
%A Mahabal, Abhijit
%A Ramachandran, Deepak
%A Bedrax-Weiss, Tania
%A Roth, Dan
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F elazar-etal-2019-large
%X Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as “Is a lion bigger than a wolf?”. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.
%R 10.18653/v1/P19-1388
%U https://aclanthology.org/P19-1388
%U https://doi.org/10.18653/v1/P19-1388
%P 3973-3983

Markdown (Informal)

[How Large Are Lions? Inducing Distributions over Quantitative Attributes](https://aclanthology.org/P19-1388) (Elazar et al., ACL 2019)

• How Large Are Lions? Inducing Distributions over Quantitative Attributes (Elazar et al., ACL 2019)

ACL

• Yanai Elazar, Abhijit Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, and Dan Roth. 2019. How Large Are Lions? Inducing Distributions over Quantitative Attributes. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3973–3983, Florence, Italy. Association for Computational Linguistics.