Por favor, use este identificador para citar o enlazar este ítem: http://repositoriodigital.ipn.mx/handle/123456789/16636
Título : Corpus-based Sentence Deletion and Split Decisions for Spanish Text Simplification
Otros títulos : Eliminación de frases y decisiones de división basadas en corpus para simplificación de textos en español
Autor : Štajner, Sanja
Drndarevic, Biljana
Saggion, Horacio
Palabras clave : Keywords. Spanish text simplification, supervised learning, sentence classification.
Fecha de publicación : 7-jun-2013
Editorial : Revista Computación y Sistemas; Vol. 17 No.2
Citación : Revista Computación y Sistemas; Vol. 17 No.2
Citación : Revista Computación y Sistemas;Vol. 17 No.2
Resumen : Abstract. This study addresses the automatic simplification of texts in Spanish in order to make them more accessible to people with cognitive disabilities. A corpus analysis of original and manually simplified news articles was undertaken in order to identify and quantify relevant operations to be implemented in a text simplification system. The articles were further compared at sentence and text level by means of automatic feature extraction and various machine learning classification algorithms, using three different groups of features (POS frequencies, syntactic information, and text complexity measures) with the aim of identifying features that help separate original documents from their simple equivalents. Finally, it was investigated whether these features can be used to decide upon simplification operations to be carried out at the sentence level (split, delete, and reduce). Automatic classification of original sentences into those to be kept and those to be eliminated outperformed the classification that was previously conducted on the same corpus. Kept sentences were further classified into those to be split or significantly reduced in length and those to be left largely unchanged, with the overall F-measure up to 0.92. Both experiments were conducted and compared on two different sets of features: all features and the best subset returned by an attribute selection algorithm.
URI : http://www.repositoriodigital.ipn.mx/handle/123456789/16636
ISSN : 1405-5546
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