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Multi-level student essay feedback in a learner corpus

P. 373–386.
Vinogradova O. I., Lyashevskaya O., Irina Panteleeva

The paper presents the results of using some computer tools and applications for the purposes of the automated and semi-automated syntactical, lexica, and error analysis of student essays in a learner corpus. The texts in the corpus were written in English by Russian learners of English. The experiment in the research consisted in comparing the parameters of different types and at different levels in the essays graded by professional examiners as the best and those graded the lowest in the pool of about 2000 essays. At the first stage in the experiment the authors applied a syntactical tool for parsing the sentences, then analyzed the results of lexical observations in those texts, and finally collected the statistics related to the errors pointed out in manual expert annotation. The parameters that had very different values for the “good” and for the “bad” essays are regarded by the authors as worthy parts of the feedback a student has to get for the text uploaded into the learner corpus.

Language: English
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Keywords: corpus researchcomputational linguisticslearner corpus
Publication based on the results of:
Лексикологические исследования на базе учебного корпуса REALEC (Learner corpus REALEC: Lexicological observations) (2016)

In book

Computational Linguistics and Intellectual Technologies. International Conference "Dialogue 2017" Proceedings
Computational Linguistics and Intellectual Technologies. International Conference "Dialogue 2017" Proceedings
Vol. 1. Issue 16 (23). , M.: -, 2017.
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