Transcending Concordance: Augmenting Academic Text for L2 Writing

Shaoqun Wu and Ian Witten
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Shaoqun Wu: Department of Computer Science, University of Waikato, Hamilton, New Zealand
Ian Witten: University of Waikato, Hamilton, New Zealand

International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 2016, vol. 6, issue 2, 1-18

Abstract: This paper describes an automated scheme that extracts salient linguistic features from academic text and presents them in an interface designed for L2 students who are learning academic writing. The system is guided by several common ways of utilizing corpus technology in L2 writing. The authors have developed and tested an extraction method that identifies typical lexico-grammatical features of any word or phrase in a corpus. Collocations and lexical bundles are automatically extracted; students can explore them by searching and browsing, and inspect them along with contextual information. They also present learners with common words, and academic words, hyperlinked to their usage and collocates in authentic contexts. This article uses a single running example, the British Academic Written English corpus, but the approach is fully automated and can be applied to any collection of English writing.

Date: 2016
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