Classifying easy-to-read texts without parsing

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

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.

Jönsson, ArneLinköpings universitet,Institutionen för datavetenskap,Tekniska högskolan (författare)
Linköpings universitetInstitutionen för datavetenskap (creator_code:org_t)

Ingår i:Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR): Association for Computational Linguistics, s. 114-1229781937284916

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Språkteknologi

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.