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    <titleInfo>
        <title>Context Effects on Human Judgments of Similarity</title>
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    <name type="personal">
        <namePart type="given">Libby</namePart>
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    <name type="personal">
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        <dateIssued>2019-08</dateIssued>
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            <title>Proceedings of the 2019 Workshop on Widening NLP</title>
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        <name type="personal">
            <namePart type="given">Diyi</namePart>
            <namePart type="family">Yang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Zeerak</namePart>
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    <abstract>The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.</abstract>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Context Effects on Human Judgments of Similarity
%A Barak, Libby
%A Kong-Johnson, Noe
%A Goldberg, Adele
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F barak-etal-2019-context
%X The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.
%U https://aclanthology.org/W19-3642
%P 135-137

Markdown (Informal)

[Context Effects on Human Judgments of Similarity](https://aclanthology.org/W19-3642) (Barak et al., WiNLP 2019)

• Context Effects on Human Judgments of Similarity (Barak et al., WiNLP 2019)

ACL

• Libby Barak, Noe Kong-Johnson, and Adele Goldberg. 2019. Context Effects on Human Judgments of Similarity. In Proceedings of the 2019 Workshop on Widening NLP, pages 135–137, Florence, Italy. Association for Computational Linguistics.