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Article published In:
International Journal of Corpus Linguistics
Vol. 26:2 (2021) ► pp.187–218

Subcategorization frame identification for learner English

ORCID logo | University of Cambridge
 | University of Birmingham
 | University of Cambridge
 | University of Cambridge
As large-scale learner corpora become increasingly available, it is vital that natural language processing (NLP) technology is developed to provide rich linguistic annotations necessary for second language (L2) research. We present a system for automatically analyzing subcategorization frames (SCFs) for learner English. SCFs link lexis with morphosyntax, shedding light on the interplay between lexical and structural information in learner language. Meanwhile, SCFs are crucial to the study of a wide range of phenomena including individual verbs, verb classes and varying syntactic structures. To illustrate the usefulness of our system for learner corpus research and second language acquisition (SLA), we investigate how L2 learners diversify their use of SCFs in text and how this diversity changes with L2 proficiency.
Keywords: subcategorization, verb-argument construction, SCF identification, second language acquisition, natural language processing
Article outline
  • 1.Introduction
  • 2.Subcategorization frames and their automatic identification
  • 3.A SCF identification system for learner English
    • 3.1Data
    • 3.2Method
    • 3.3Training and evaluation
      • 3.3.1Accuracy
      • 3.3.2Error analysis
        • i.Distinction between arguments and adjuncts
        • ii.Prepositional attachment
  • 4.Case study: SCF diversity and L2 proficiency
    • 4.1Design of SCF diversity metrics
      • 4.1.1Basic design
        • i.Repetition
        • ii.Evenness
        • iii.Dispersion
        • iv.Disparity
      • 4.1.2Control for text length
    • 4.2Data selection and statistical analysis method
    • 4.3Results
  • 5.Conclusion
  • Notes
  • References
Published online: 8 December 2020
DOI logo
https://doi.org/10.1075/ijcl.18097.hua
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