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        <title>Measuring Text Complexity for Italian as a Second Language Learning Purposes</title>
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        <namePart type="given">Luciana</namePart>
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    <name type="personal">
        <namePart type="given">Stefania</namePart>
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            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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    <abstract>The selection of texts for second language learning purposes typically relies on teachers’ and test developers’ individual judgment of the observable qualitative properties of a text. Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility. This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning. A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts. The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.</abstract>
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%0 Conference Proceedings
%T Measuring Text Complexity for Italian as a Second Language Learning Purposes
%A Forti, Luciana
%A Milani, Alfredo
%A Piersanti, Luisa
%A Santarelli, Filippo
%A Santucci, Valentino
%A Spina, Stefania
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F forti-etal-2019-measuring
%X The selection of texts for second language learning purposes typically relies on teachers’ and test developers’ individual judgment of the observable qualitative properties of a text. Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility. This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning. A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts. The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.
%R 10.18653/v1/W19-4438
%U https://aclanthology.org/W19-4438
%U https://doi.org/10.18653/v1/W19-4438
%P 360-368

Markdown (Informal)

[Measuring Text Complexity for Italian as a Second Language Learning Purposes](https://aclanthology.org/W19-4438) (Forti et al., BEA 2019)

• Measuring Text Complexity for Italian as a Second Language Learning Purposes (Forti et al., BEA 2019)

ACL

• Luciana Forti, Alfredo Milani, Luisa Piersanti, Filippo Santarelli, Valentino Santucci, and Stefania Spina. 2019. Measuring Text Complexity for Italian as a Second Language Learning Purposes. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 360–368, Florence, Italy. Association for Computational Linguistics.