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    <titleInfo>
        <title>Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words</title>
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    <name type="personal">
        <namePart type="given">Alice</namePart>
        <namePart type="family">Pintard</namePart>
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            <title>Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)</title>
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        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Gala</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rodrigo</namePart>
            <namePart type="family">Wilkens</namePart>
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    <abstract>Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).</abstract>
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        <url>https://aclanthology.org/2020.readi-1.13</url>
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        <date>2020-05</date>
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            <start>85</start>
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%0 Conference Proceedings
%T Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words
%A Pintard, Alice
%A François, Thomas
%Y Gala, Núria
%Y Wilkens, Rodrigo
%S Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-45-0
%G English
%F pintard-francois-2020-combining
%X Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).
%U https://aclanthology.org/2020.readi-1.13
%P 85-92

Markdown (Informal)

[Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words](https://aclanthology.org/2020.readi-1.13) (Pintard & François, READI 2020)

• Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words (Pintard & François, READI 2020)

ACL

• Alice Pintard and Thomas François. 2020. Combining Expert Knowledge with Frequency Information to Infer CEFR Levels for Words. In Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI), pages 85–92, Marseille, France. European Language Resources Association.