Approaching Language Transfer through Text Classification : : Explorations in the Detection-based Approach / / ed. by Scott Jarvis, Scott A. Crossley.

• Superior document: Title is part of eBook package: De Gruyter MultiLingual Matters Backlist eBook-Package 2000-2013
• MitwirkendeR: Bestgen, Yves,; Castañeda-Jiménez, Gabriela,; Crossley, Scott A.,; Granger, Sylviane,; Jarvis, Scott,; McNamara, Danielle S.,; McNamara, Danielle,; Nielsen, Rasmus,; Paquot, Magali,; Thewissen, Jennifer,
• HerausgeberIn: Crossley, Scott A.,; Jarvis, Scott,
• Place / Publishing House: Bristol ;, Blue Ridge Summit : : Multilingual Matters, , [2012]; ©2012
• Year of Publication: 2012
• Language: English
• Series: Second Language Acquisition
• Physical Description: 1 online resource (208 p.)

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.

Table of Contents

Frontmatter -- Contents -- Contributors -- 1. The Detection-Based Approach: An Overview -- 2. Detecting L2 Writers’ L1s on the Basis of Their Lexical Styles -- 3. Exploring the Role of n-Grams in L1 Identification -- 4. Detecting the First Language of Second Language Writers Using Automated Indices of Cohesion, Lexical Sophistication, Syntactic Complexity and Conceptual Knowledge -- 5. Error Patterns and Automatic L1 Identification -- 6. The Comparative and Combined Contributions of n-Grams, Coh-Metrix Indices and Error Types in the L1 Classification of Learner Texts -- 7. Detection-Based Approaches: Methods, Theories and Applications