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arXiv · 1704.03013

Automatic Classification of the Complexity of Nonfiction Texts in Portuguese for Early School Years

Nathan Siegle Hartmann·Livia Cucatto·Danielle Brants·Sandra Aluísio
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Abstract

Recent research shows that most Brazilian students have serious problems regarding their reading skills. The full development of this skill is key for the academic and professional future of every citizen. Tools for classifying the complexity of reading materials for children aim to improve the quality of the model of teaching reading and text comprehension. For English, Fengs work [11] is considered the state-of-art in grade level prediction and achieved 74% of accuracy in automatically classifying 4 levels of textual complexity for close school grades. There are no classifiers for nonfiction texts for close grades in Portuguese. In this article, we propose a scheme for manual annotation of texts in 5 grade levels, which will be used for customized reading to avoid the lack of interest by students who are more advanced in reading and the blocking of those that still need to make further progress. We obtained 52% of accuracy in classifying texts into 5 levels and 74% in 3 levels. The results prove to be promising when compared to the state-of-art work.9

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Nathan Siegle Hartmann, Livia Cucatto, Danielle Brants, Sandra Aluísio. 2017-04-10. Automatic Classification of the Complexity of Nonfiction Texts in Portuguese for Early School Years. https://doi.org/10.1007/978-3-319-41552-9_2

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