1.

Record Nr.

UNINA9910779377603321

Titolo

Approaching language transfer through text classification [[electronic resource] ] : explorations in the detection-based approach / / edited by Scott Jarvis and Scott A. Crossley

Pubbl/distr/stampa

Bristol ; ; Buffalo, : Multilingual Matters, c2012

ISBN

9781847696991

9786613770448

1-84769-699-6

Descrizione fisica

1 online resource (196 p.)

Collana

Second language acquisition ; ; 64

Classificazione

ES 760

Altri autori (Persone)

JarvisScott <1966->

CrossleyScott A

Disciplina

401/.93

Soggetti

Language transfer (Language learning)

English language - Rhetoric - Study and teaching

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

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

Sommario/riassunto

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.