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Full-Text Articles in Computational Linguistics
Quantifying The Development Of Phraseological Competence In L2 English Writing: An Automated Approach, Yves Bestgen, Sylviane Granger
Quantifying The Development Of Phraseological Competence In L2 English Writing: An Automated Approach, Yves Bestgen, Sylviane Granger
Yves Bestgen
Based on the large body of research that shows phraseology to be pervasive in language, this study aims to assess the role played by phraseological competence in the development of L2 writing proficiency and text quality assessment. We propose to use CollGram, a technique that assigns to each pair of contiguous words (bigrams) in a learner text two association scores (mutual information and t-score) computed on the basis of a large reference corpus, the Corpus of Contemporary American English. Applied to the Michigan State University Corpus of second language writing, CollGram shows a longitudinal decrease in the use of collocations …
Automated Diagnostic Writing Tests: Why? How?, Elena Cotos, Nick Pendar
Automated Diagnostic Writing Tests: Why? How?, Elena Cotos, Nick Pendar
Elena Cotos
Diagnostic language assessment can greatly benefit from a collaborative union of computer-assisted language testing (CALT) and natural language processing (NLP). Currently, most CALT applications mainly allow for inferences about L2 proficiency based on learners’ recognition and comprehension of linguistic input and hardly concern language production (Holland, Maisano, Alderks, & Martin, 1993). NLP is now at a stage where it can be used or adapted for diagnostic testing of learner production skills. This paper explores the viability of NLP techniques for the diagnosis of L2 writing by analyzing the state of the art in current diagnostic language testing, reviewing the existing …
Automatic Identification Of Discourse Moves In Scientific Article Introductions, Elena Cotos, Nick Pendar
Automatic Identification Of Discourse Moves In Scientific Article Introductions, Elena Cotos, Nick Pendar
Elena Cotos
This paper reports on the first stage of building an educational tool for international graduate students to improve their academic writing skills. Taking a text-categorization approach, we experimented with several models to automatically classify sentences in research article introductions into one of three rhetorical moves. The paper begins by situating the project within the larger framework of intelligent computer-assisted language learning. It then presents the details of the study with very encouraging results. The paper then concludes by commenting on how the system may be improved and how the project is intended to be pursued and evaluated.