A Corpus-Informed Text Reconstruction Resource for Learning About the Language of Scientific Abstracts

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Abstract

Both reading and writing abstracts require specific language skills and conceptual capacities, which may challenge advanced learners. This paper draws explicitly upon the Emergence and Scientext research projects which focused on the lexis of scientific texts in French and English. The teaching objective of the project described here was to create a collection of text reconstruction tasks targeting the patterns of English that are uncommon in French. These tasks are to be integrated within the platform Enigma Plus (http://elang.ujf-grenoble.fr/enigma/). The current project is the conception of a new module based on data-driven materials collected from Scientext, a corpus of medical and biology abstracts in English (http://scientext.msh-alpes.fr/scientext-site-en/spip.php?article9). This paper discusses the task focusing on the word hypothesis, the first of a dozen tasks based on authentic examples and designed to help learners of English as a foreign language to better read and write science abstracts. The results revealed several similarities and contrasts with the French findings. These results were integrated into the text reconstruction task. Findings of user practices reported in previous studies were taken into account to optimize completion of the task by the widest range of user practices and errors.

Keywords: corpora, abstracts, on-line text reconstruction, English for specific purposes, English as a foreign language.

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Hartwell, Laura M.; Jacques, Marie-Paule. (2012). A Corpus-Informed Text Reconstruction Resource for Learning About the Language of Scientific Abstracts. In Linda Bradley, Sylvie Thouësny (Eds), CALL: Using, Learning, Knowing, EUROCALL Conference, Gothenburg, Sweden, 22-25 August 2012, Proceedings (pp. 117-123). Research-publishing.net. https://doi.org/10.14705/rpnet.2012.000037

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