Description
Book SynopsisRecent 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.
Trade ReviewIn this bold and pioneering interdisciplinary study, experts on SLA research, computational analysis and statistics collaborate to try to identify the L1 background of non-native writers. The result is a most impressive work which will take the field of crosslinguistic studies a long way forward. A MUST for all SLA researchers!
-- Håkan Ringbom, Emeritus Professor, Åbo Akademi University, Finland
This is an articulate, comprehensive, and timely volume on a fascinating yet largely underexplored area. Jarvis and Crossley have produced an impressive collection of research-based evidence on language transfer using a corpus-based approach. The volume is a must-have for students, scholars, and practitioners interested in language transfer, corpus linguistics, forensic linguistics, text classification, second language writing, error analysis, and language assessment.
-- Pavel Trofimovich, Concordia University, Canada
This book indeed opens a new path in the study of language transfer. The investigations brought together here combine the strengths of earlier work on transfer with those of two other fields, computational linguistics and corpus research, which have much to offer students and researchers interested in second language acquisition and multilingualism. There can be little doubt that the tools used in this seminal work will continue to offer important insights for a long time to come.
-- Terence Odlin, Ohio State University, USA
Table of Contents1 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
