Volume 64 in the Second Language Acquisition Series.

Includes bibliographical references and index.

"Recent work has pointed to the need for a detection-based approach to transfer capable of discovering elusive crosslinguistic effects through the use of human judges and computer classifiers that can learn to predict learners’ language backgrounds based on their patterns of language use. This book addresses that need. It details the nature of the detection-based approach, discusses how this approach fits into the overall scope of transfer research, and discusses the few previous studies that have laid the groundwork for this approach. The core of the book consists of five empirical studies that use computer classifiers to detect the native-language affiliations of texts written by foreign language learners of English. The results highlight combinations of language features that are the most reliable predictors of learners’ language backgrounds." (Book Cover)

CONTENTS:

1. The Detection-Based Approach: An Overview / Scott Jarvis

2. Detecting L2 Writers' L1s on the Basis of Their Lexical Styles / Scott Jarvis, Gabriela Castaneda-Jiménez and Rasmus Nielsen

3. Exploring the Role of n-Grams in L1 Identification / Scott Jarvis and Magali Paquot

4. Detecting the First Language of Second Language Writers Using Automated Indices of Cohesion, Lexical Sophistication, Syntactic Complexity and Conceptual Knowledge / Scott A. Crossley and Danielle S. McNamara

5. Error Patterns and Automatic L1 Identification / Yves Bestgen, Sylvian Granger and Jennifer Thewissen

6. The Comparative and Combined Contributions of n-Grams, Coh-Metrix Indices and Error Types in the L1 Classification of Learner Texts / Scott Jarvis, Yves Bestgen, Scott A. Crossley, Sylviane Granger, Magali Paquot, Jennifer Thewissen and Danielle McNamara

7. Detection-Based Approaches: Methods, Theories and Applications / Scott A. Crossley