Automatic Text Difficulty Classifier - Assisting the Selection Of Adequate Reading Materials For European Portuguese Teaching

Pedro Curto, Nuno Mamede, Jorge Baptista

Abstract

This paper describes a system to assist the selection of adequate reading materials to support European Portuguese teaching, especially as second language, while highlighting the key challenges on the selection of linguistic features for text difficulty (readability) classification. The system uses existing Natural Language Processing (NLP) tools to extract linguistic features from texts, which are then used by an automatic readability classifier. Currently, 52 features are extracted: parts-of-speech (POS), syllables, words, chunks and phrases, averages and frequencies, and some extra features. A classifier was created using these features and a corpus, previously annotated by readability level, using a five-levels language classification official standard for Portuguese as Second Language. In a five-levels (from A1 to C1) scenario, the best-performing learning algorithm (LogitBoost) achieved an accuracy of 75.11% with a root mean square error (RMSE) of 0.269. In a three-levels (A, B and C) scenario, the best-performing learning algorithm (C4.5 grafted) achieved 81.44% accuracy with a RMSE of 0.346.

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Paper Citation


in Harvard Style

Curto P., Mamede N. and Baptista J. (2015). Automatic Text Difficulty Classifier - Assisting the Selection Of Adequate Reading Materials For European Portuguese Teaching . In Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-107-6, pages 36-44. DOI: 10.5220/0005428300360044


in Bibtex Style

@conference{csedu15,
author={Pedro Curto and Nuno Mamede and Jorge Baptista},
title={Automatic Text Difficulty Classifier - Assisting the Selection Of Adequate Reading Materials For European Portuguese Teaching},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2015},
pages={36-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005428300360044},
isbn={978-989-758-107-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Automatic Text Difficulty Classifier - Assisting the Selection Of Adequate Reading Materials For European Portuguese Teaching
SN - 978-989-758-107-6
AU - Curto P.
AU - Mamede N.
AU - Baptista J.
PY - 2015
SP - 36
EP - 44
DO - 10.5220/0005428300360044