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Classifying easy-to...
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Falkenjack, Johan,1986-Linköpings universitet,Institutionen för datavetenskap,Tekniska högskolan
(författare)
Classifying easy-to-read texts without parsing
- Artikel/kapitelEngelska2014
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Association for Computational Linguistics,2014
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LIBRIS-ID:oai:DiVA.org:liu-117547
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-117547URI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:kon swepub-publicationtype
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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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Jönsson, ArneLinköpings universitet,Institutionen för datavetenskap,Tekniska högskolan
(författare)
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Linköpings universitetInstitutionen för datavetenskap
(creator_code:org_t)
Sammanhörande titlar
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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
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