Approaching Language Transfer through Text Classification

With contributions from 4 members of the CECL team: Yves Bestgen, Sylviane Granger, Magali Paquot and Jennifer Thewissen
"Approaching Language Transfer through Text Classification" explains the detection-based approach to investigating crosslinguistic influence and illustrates the value of the approach through a collection of five empirical studies that use the approach to quantify, evaluate, and isolate the subtle and complex infl uences of learners’ native-language backgrounds on their English writing.

Contents

  1. Scott Jarvis: The Detection-Based Approach: An Overview
  2. Scott Jarvis, Gabriela Castañeda-Jiménez and Rasmus Nielsen: Detecting L2 Writers’ L1s on the Basis of their Lexical Styles
  3. Scott Jarvis and Magali Paquot: Exploring the Role of N-Grams in L1 Identification
  4. Scott A. Crossley and Danielle S. McNamara: Detecting the First Language of Second Language Writers: Using Automated Indices of Cohesion, Lexical Sophistication, Syntactic Complexity, and Conceptual Knowledge
  5. Yves Bestgen, Sylviane Granger and Jennifer Thewissen: Error Patterns and Automatic L1 Identification
  6. Scott Jarvis, Yves Bestgen, Scott A. Crossley, Sylviane Granger, Magali Paquot, Jennifer Thewissen and Danielle S. McNamara: The Comparative and Combined Contributions of N-grams, Coh-Metrix Indices, and Error Types in the L1 Classification of Learner Texts
  7. Scott A. Crossley: Detection-Based Approaches: Methods, Theories and Applications