Automatic Treatment and Analysis of Learner Corpus Data

Editors

Ana Díaz-Negrillo | University of Granada

Nicolas Ballier | University of Paris Diderot

Paul Thompson | University of Birmingham

Hardbound – Available

ISBN 9789027203663 | EUR 95.00 | USD 143.00

e-Book –

ISBN 9789027270955 | EUR 95.00 | USD 143.00

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

https://doi.org/10.1075/scl.59

Table of Contents

Section 1. Introduction

Introduction

Nicolas Ballier, Ana Díaz-Negrillo and Paul Thompson | pp. 3–8

Learner corpora: Looking towards the future

Ana Díaz-Negrillo and Paul Thompson | pp. 9–30

Section 2. Compilation, annotation and exchangeability of learner corpus data

Developing corpus interoperability for phonetic investigation of learner corpora

Nicolas Ballier and Philippe Martin | pp. 33–64

Learner corpora and second language acquisition: The design and collection of CEDEL2

Cristóbal Lozano and Amaya Mendikoetxea | pp. 65–100

Competing target hypotheses in the Falko corpus: A flexible multi-layer corpus architecture

Marc Reznicek, Anke Lüdeling and Hagen Hirschmann | pp. 101–124

Section 3. Automatic approaches to the identification of learner language features in learner corpus data

Using learner corpora for automatic error detection and correction

Michael Gamon, Martin Chodorow, Claudia Leacock and Joel Tetreault | pp. 127–150

Automatic suprasegmental parameter extraction in learner corpora

Emmanuel Ferragne | pp. 151–168

Criterial feature extraction using parallel learner corpora and machine learning

Yukio Tono | pp. 169–204

Section 4. Analysis of learner corpus data

Phonological acquisition in the French-English interlanguage: Rising above the phoneme

Adrien Meli | pp. 207–226

Prosody in a contrastive learner corpus

Anne Tortel | pp. 227–248

A corpus-based comparison of syntactic complexity in NNS and NS university students’ writing

Haiyang Ai and Xiaofei Lu | pp. 249–264

Analysing coherence in upper-intermediate learner writing

Barbara Schiftner | pp. 265–286

Statistical tests for the analysis of learner corpus data

Stefan Th. Gries | pp. 287–310

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

Cited by 12 other publications

Order by:

Ballier, Nicolas, Stéphane Canu, Caroline Petitjean, Gilles Gasso, Carlos Balhana, Theodora Alexopoulou & Thomas Gaillat

2020. Machine learning for learner English. International Journal of Learner Corpus Research 6:1 ► pp. 72 ff.

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.

Herment, Sophie

2018. Apprentissage et enseignement de la prosodie : l’importance de la visualisation. Revue française de linguistique appliquée Vol. XXIII:1 ► pp. 73 ff.

Liontou, Trisevgeni

2022. Automated Discourse Analysis Techniques and Implications for Writing Assessment. Languages 8:1 ► pp. 3 ff.

Mairano, Paolo, Bene Bassetti, Mirjana Sokolović-Perović & Tania Cerni

2018. Effects of L1 orthography and L1 phonology on L2 English pronunciation. Revue française de linguistique appliquée Vol. XXIII:1 ► pp. 45 ff.

Mendikoetxea, Amaya

2013. Corpus‐Based Research in Second Language Spanish 1 . 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. Journal of Biomechanical Engineering 143:12

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. The LANGSNAP longitudinal learner corpus. 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 16 april 2024. 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.

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