Artículos

 

Corpus-based Sentence Deletion and Split Decisions for Spanish Text Simplification

 

Eliminación de frases y decisiones de división basadas en corpus para simplificación de textos en español

 

Sanja Štajner¹, Biljana Drndarević², and Horacio Saggion³

 

¹ Research Group in Computational Linguistics, University of Wolverhampton, United Kingdom sanjastajner@wlv.ac.uk

² TALN, Department of Information and Communication Technology, Universitat Pompeu Fabra, Spain

³ TALN, Department of Information and Communication Technology, Universitat Pompeu Fabra, Spain

 

Article received on 13/12/2012
Accepted on 25/01/2013.

 

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.

Keywords: Spanish text simplification, supervised learning, sentence classification.

 

Resumen

Este estudio aborda el problema de simplificación automática de textos en español con el fin de hacerlos más accesible a las personas con discapacidades cognitivas. Análisis de corpus de artículos originales y artículos simplificados manualmente se ha realizado para identificar y calificar relevantes operaciones que tienen que ser implementadas en el sistema de simplificación de textos. Luego los artículos se han comparado al nivel de frase y texto mediante extracción automática de características y diversos algoritmos de aprendizaje de máquina para clasificación usando tres distintos grupos de características (frecuencias de partes de oración (POS), información sintáctica y medidas de la complejidad de textos) con el propósito de identificar las características que ayuden a distinguir los documentos originales de sus simples equivalentes. Finalmente, se ha investigado la posibilidad de usar esas características en operaciones de simplificación a nivel de frase (dividir, eliminar y reducir). Clasificación automática de frases originales en las que deben preservarse y las que deben eliminarse ha superado la clasificación anterior sobre el mismo corpus. Las frases guardadas luego se clasificaron en las que se dividen o reducen de manera significativa en su longitud y las que se quedan sin cambios mayores con la F-medida de 0.92. Ambos experimentos se realizaron y compararon sobre dos distintos conjuntos de características: el de todas características y el mejor subconjunto recuperado por el algoritmo de selección de atributos.

Palabras clave: Simplificación de textos en español, aprendizaje supervisado, clasificación de frases.

 

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Acknowledgments

The research described in this paper was partially funded by the European Commission under the Seventh (FP7 - 2007-2013) Framework Programme for Research and Technological Development (FIRST 287607). This publication [communication] reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. We acknowledge partial support from the following grants: Avanza Competitiveness grant number TSI-020302-2010-84 from the Ministry of Industry, Tourism and Trade, Spain and grant number TIN2012-38584-C06-03 and fellowship RYC-2009-04291 (Programa Ramón y Cajal 2009) from the Spanish Ministry of Economy and Competitiveness.

 

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