Classifying easy-to-read texts without parsing

Falkenjack, Johan, 1986- (author): Linköpings universitet,Institutionen för datavetenskap,Tekniska högskolan

Jönsson, Arne (author): Linköpings universitet,Institutionen för datavetenskap,Tekniska högskolan

(creator_code:org_t)

Association for Computational Linguistics, 2014
2014
English.
In: Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR). - : Association for Computational Linguistics. - 9781937284916 ; , s. 114-122

Abstract Subject headings

Document classification using automated linguistic analysis and machine learning (ML) has been shown to be a viable road forward for readability assessment. The best models can be trained to decide if a text is easy to read or not with very high accuracy, e.g. a model using 117 parameters from shallow, lexical, morphological and syntactic analyses achieves 98,9% accuracy. In this paper we compare models created by parameter optimization over subsets of that total model to find out to which extent different high-performing models tend to consist of the same parameters and if it is possible to find models that only use features not requiring parsing. We used a genetic algorithm to systematically optimize parameter sets of fixed sizes using accuracy of a Support Vector Machine classi- fier as fitness function. Our results show that it is possible to find models almost as good as the currently best models while omitting parsing based features.

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Language Technol ...

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