Classifying easy-to-read texts without parsing

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

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

(creator_code:org_t)

Association for Computational Linguistics, 2014
2014
Engelska.
Ingår i: 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 Ämnesord

Stäng

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.

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