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- W2133944470 abstract "Several sets of explanatory variables - including shallow, language modeling, POS, syntactic, and discourse features - are compared and evaluated in terms of their impact on predicting the grade level of reading material for primary school students. We find that features based on in-domain language models have the highest predictive power. Entity-density (a discourse feature) and POS-features, in particular nouns, are individually very useful but highly correlated. Average sentence length (a shallow feature) is more useful - and less expensive to compute - than individual syntactic features. A judicious combination of features examined here results in a significant improvement over the state of the art." @default.
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- W2133944470 date "2010-08-23" @default.
- W2133944470 modified "2023-10-01" @default.
- W2133944470 title "A Comparison of Features for Automatic Readability Assessment" @default.
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