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In:Beyond Concordance Lines: Corpora in language education
Edited by Pascual Pérez-Paredes and Geraldine Mark
[Studies in Corpus Linguistics 102] 2021
► pp. 121–148

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Chapter 6
Profiling learners through pragmatically and error annotated corpora

ORCiD logo with link | University of Bayreuth
Published online: 22 December 2021
DOI logo with link
https://doi.org/10.1075/scl.102.06wei
Abstract
This proof-of-concept study presents a novel way of analysing learner language based on 20 randomly selected interviews from the Chinese part of the Louvain International Database of Spoken English Interlanguage (LINDSEI) (Gilquin et al., 2010), annotated both pragmatically and using a newly devised error analysis scheme. Annotation and initial analyses were primarily conducted in the Dialogue Annotation and Research Tool (DART) (Weisser, 2016b), and the relevant features extracted and normed by the number of functional (speech act) units to obtain meaningful comparable results across speakers. The results indicate that the majority of errors affect the coherence of the learners’ narrative and that the communicative strategies used by the learners exhibit a high number of discourse markers or response-signals used (em)phatically, apparently only serving as ‘planning facilitators’, rather than genuine structural or interactional devices.
Article outline
  • Introduction
  • Methods
    • Data preparation
    • Annotation and extraction
  • Types of annotation
    • The feature tagset
    • The DART speech-act taxonomy
  • Results and discussion
    • Syntactic unit type frequencies
    • Speech acts corresponding to DMs and yes-units
    • Error distributions and characteristics
      • Grouped error distribution
      • Coherence errors and their potential effects
        • Tense-, mood-, or aspect-related issues
        • Use of connectors
        • Omissions or misuse of prepositions
        • Inconsistent or unclear anaphoric reference
    • Lexical infelicities
    • Specifier errors
  • Conclusion
  • References
References (28)
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Granger, Sylviane
2024. From early to future learner corpus research. International Journal of Learner Corpus Research 10:2 ► pp. 247 ff. DOI logo

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