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    <titleInfo>
        <title>Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model</title>
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    <name type="personal">
        <namePart type="given">Masayuki</namePart>
        <namePart type="family">Asahara</namePart>
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            <title>Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP</title>
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        <name type="personal">
            <namePart type="given">Silviu</namePart>
            <namePart type="family">Paun</namePart>
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        <name type="personal">
            <namePart type="given">Dirk</namePart>
            <namePart type="family">Hovy</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This paper presents research on word familiarity rate estimation using the ‘Word List by Semantic Principles’. We collected rating information on 96,557 words in the ‘Word List by Semantic Principles’ via Yahoo! crowdsourcing. We asked 3,392 subject participants to use their introspection to rate the familiarity of words based on the five perspectives of ‘KNOW’, ‘WRITE’, ‘READ’, ‘SPEAK’, and ‘LISTEN’, and each word was rated by at least 16 subject participants. We used Bayesian linear mixed models to estimate the word familiarity rates. We also explored the ratings with the semantic labels used in the ‘Word List by Semantic Principles’.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5902</identifier>
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        <url>https://aclanthology.org/D19-5902</url>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model
%A Asahara, Masayuki
%Y Paun, Silviu
%Y Hovy, Dirk
%S Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F asahara-2019-word
%X This paper presents research on word familiarity rate estimation using the ‘Word List by Semantic Principles’. We collected rating information on 96,557 words in the ‘Word List by Semantic Principles’ via Yahoo! crowdsourcing. We asked 3,392 subject participants to use their introspection to rate the familiarity of words based on the five perspectives of ‘KNOW’, ‘WRITE’, ‘READ’, ‘SPEAK’, and ‘LISTEN’, and each word was rated by at least 16 subject participants. We used Bayesian linear mixed models to estimate the word familiarity rates. We also explored the ratings with the semantic labels used in the ‘Word List by Semantic Principles’.
%R 10.18653/v1/D19-5902
%U https://aclanthology.org/D19-5902
%U https://doi.org/10.18653/v1/D19-5902
%P 6-14

Markdown (Informal)

[Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model](https://aclanthology.org/D19-5902) (Asahara, 2019)

• Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model (Asahara, 2019)

ACL

• Masayuki Asahara. 2019. Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model. In Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP, pages 6–14, Hong Kong. Association for Computational Linguistics.