Automatic Treatment and Analysis of Learner Corpus Data
Editors
| University of Granada
| University of Paris Diderot
| University of Birmingham
This book is a critical appraisal of recent developments in corpus linguistics for the analysis of written and spoken learner data. The twelve papers cover an introductory critical appraisal of learner corpus data compilation and development (section 1); issues in data compilation, annotation and exchangeability (section 2); automatic approaches to data identification and analysis (section 3); and analysis of learner corpus data in the light of recent models of data analysis and interpretation, especially recent automatic approaches for the identification of learner language features (section 4). This collection is aimed at students and researchers of corpus linguistics, second language acquisition studies and quantitative linguistics. It will significantly advance learner corpus research in terms of methodological innovation and will fill in an important gap in the development of multidisciplinary approaches (for learner corpus studies).
[Studies in Corpus Linguistics, 59] 2013. vi, 314 pp.
Publishing status: Available
© John Benjamins Publishing Company
Table of Contents
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Section 1. Introduction
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IntroductionNicolas Ballier, Ana Díaz-Negrillo and Paul Thompson | pp. 3–8
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Learner corpora: Looking towards the futureAna Díaz-Negrillo and Paul Thompson | pp. 9–30
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Section 2. Compilation, annotation and exchangeability of learner corpus data
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Developing corpus interoperability for phonetic investigation of learner corporaNicolas Ballier and Philippe Martin | pp. 33–64
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Learner corpora and second language acquisition: The design and collection of CEDEL2Cristóbal Lozano and Amaya Mendikoetxea | pp. 65–100
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Competing target hypotheses in the Falko corpus: A flexible multi-layer corpus architectureMarc Reznicek, Anke Lüdeling and Hagen Hirschmann | pp. 101–124
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Section 3. Automatic approaches to the identification of learner language features in learner corpus data
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Using learner corpora for automatic error detection and correctionMichael Gamon, Martin Chodorow, Claudia Leacock and Joel Tetreault | pp. 127–150
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Automatic suprasegmental parameter extraction in learner corporaEmmanuel Ferragne | pp. 151–168
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Criterial feature extraction using parallel learner corpora and machine learningYukio Tono | pp. 169–204
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Section 4. Analysis of learner corpus data
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Phonological acquisition in the French-English interlanguage: Rising above the phonemeAdrien Meli | pp. 207–226
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Prosody in a contrastive learner corpusAnne Tortel | pp. 227–248
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A corpus-based comparison of syntactic complexity in NNS and NS university students’ writingHaiyang Ai and Xiaofei Lu | pp. 249–264
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Analysing coherence in upper-intermediate learner writingBarbara Schiftner | pp. 265–286
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Statistical tests for the analysis of learner corpus dataStefan Th. Gries | pp. 287–310
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Index | pp. 311–314
“This book is an excellent volume that pushes forward the scope of learner corpus processing and analysis, by bridging learner corpus design with its automatic analysis. I know of no other work which contains such breadth, covering for example both spoken and written data, while containing enough depth for even the experienced researcher. I expect to refer to this book for years.”
Markus Dickinson, Indiana University
Cited by
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2020. Machine learning for learner English.
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Haser, Verena, Anita Auer, Bert Botma, Beáta Gyuris, Kathryn Allan, Mackenzie Kerby, Lieselotte Anderwald, Alexander Kautzsch, Maja Miličević, Tihana Kraš & Marcus Callies
2015. IEnglish Language.
The Year's Work in English Studies 94:1 ► pp. 1 ff.
Mendikoetxea, Amaya
2013.
In The Handbook of Spanish Second Language Acquisition,
► pp. 9 ff.
Pan, Jun, Billy Tak-Ming Wong & Honghua Wang
2022. Navigating learner data in translator and interpreter training.
Babel. Revue internationale de la traduction / International Journal of Translation 68:2 ► pp. 236 ff.
Reed, Elisabeth C., Donna Kain & Stephanie M. George
2021. The Relationship Between Perceived Confidence, Gender, and Writing in a Biomedical Engineering Research Experience for Undergraduates Site.
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Schneider, Gerold & Gaëtanelle Gilquin
2016. Detecting innovations in a parsed corpus of learner English.
International Journal of Learner Corpus Research 2:2 ► pp. 177 ff.
Tracy-Ventura, Nicole, Rosamond Mitchell & Kevin McManus
2016.
In Spanish Learner Corpus Research [Studies in Corpus Linguistics, 78],
► pp. 117 ff.
Trouvain, Jürgen, Frank Zimmerer, Bernd Möbius, Mária Gósy & Anne Bonneau
2017. Segmental, prosodic and fluency features in phonetic learner corpora.
International Journal of Learner Corpus Research 3:2 ► pp. 105 ff.
Wisniewski, Katrin
2022. Gesprochene Lernerkorpora des Deutschen: Eine Bestandsaufnahme.
Zeitschrift für germanistische Linguistik 50:1 ► pp. 1 ff.
This list is based on CrossRef data as of 28 july 2022. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.
Subjects & Metadata
Linguistics
BIC Subject: CFDC – Language acquisition
BISAC Subject: LAN009000 – LANGUAGE ARTS & DISCIPLINES / Linguistics / General