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        <title>Implicit readability ranking using the latent variable of a Bayesian Probit model</title>
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            <namePart type="given">Dominique</namePart>
            <namePart type="family">Brunato</namePart>
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            <namePart type="given">Giulia</namePart>
            <namePart type="family">Venturi</namePart>
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            <namePart type="given">Thomas</namePart>
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    <abstract>Data driven approaches to readability analysis for languages other than English has been plagued by a scarcity of suitable corpora. Often, relevant corpora consist only of easy-to-read texts with no rank information or empirical readability scores, making only binary approaches, such as classification, applicable. We propose a Bayesian, latent variable, approach to get the most out of these kinds of corpora. In this paper we present results on using such a model for readability ranking. The model is evaluated on a preliminary corpus of ranked student texts with encouraging results. We also assess the model by showing that it performs readability classification on par with a state of the art classifier while at the same being transparent enough to allow more sophisticated interpretations.</abstract>
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%0 Conference Proceedings
%T Implicit readability ranking using the latent variable of a Bayesian Probit model
%A Falkenjack, Johan
%A Jönsson, Arne
%Y Brunato, Dominique
%Y Dell’Orletta, Felice
%Y Venturi, Giulia
%Y François, Thomas
%Y Blache, Philippe
%S Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F falkenjack-jonsson-2016-implicit
%X Data driven approaches to readability analysis for languages other than English has been plagued by a scarcity of suitable corpora. Often, relevant corpora consist only of easy-to-read texts with no rank information or empirical readability scores, making only binary approaches, such as classification, applicable. We propose a Bayesian, latent variable, approach to get the most out of these kinds of corpora. In this paper we present results on using such a model for readability ranking. The model is evaluated on a preliminary corpus of ranked student texts with encouraging results. We also assess the model by showing that it performs readability classification on par with a state of the art classifier while at the same being transparent enough to allow more sophisticated interpretations.
%U https://aclanthology.org/W16-4112
%P 104-112

Markdown (Informal)

[Implicit readability ranking using the latent variable of a Bayesian Probit model](https://aclanthology.org/W16-4112) (Falkenjack & Jönsson, CL4LC 2016)

• Implicit readability ranking using the latent variable of a Bayesian Probit model (Falkenjack & Jönsson, CL4LC 2016)

ACL

• Johan Falkenjack and Arne Jönsson. 2016. Implicit readability ranking using the latent variable of a Bayesian Probit model. In Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), pages 104–112, Osaka, Japan. The COLING 2016 Organizing Committee.