Abstract
In order to determine the effect of phrasal features in automated essay scoring (AES) of English writing by Chinese college students, a multiple linear regression model is adopted involving lexical features and phrasal features. A corpus of college English writing containing 660 compositions was used as training and testing texts. The experimental results show that with phrasal features, all indexes including precision, recall, total accuracy and total wrong ratio of the AES model are improved. Especially obvious is the total accuracy, which improves from 38.64% to 43.18%. Though the regression model cannot be applied into practical use yet, it lays a solid foundation for further research in AES field.
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Ge, S. (2013). Automatic Scoring of English Writing Based on Joint of Lexical and Phrasal Features. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_18
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DOI: https://doi.org/10.1007/978-3-642-53932-9_18
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