LEADER 03553nam 2200613 a 450 001 9910452565403321 005 20200520144314.0 010 $a1-280-99883-0 010 $a9786613770448 010 $a1-84769-699-6 024 7 $a10.21832/9781847696991 035 $a(CKB)2550000000108262 035 $a(EBL)977734 035 $a(OCoLC)806204967 035 $a(MiAaPQ)EBC977734 035 $a(DE-B1597)491364 035 $a(OCoLC)1043622844 035 $a(DE-B1597)9781847696991 035 $a(Au-PeEL)EBL977734 035 $a(CaPaEBR)ebr10582794 035 $a(CaONFJC)MIL377044 035 $a(EXLCZ)992550000000108262 100 $a20111130d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApproaching language transfer through text classification$b[electronic resource] $eexplorations in the detection-based approach /$fedited by Scott Jarvis and Scott A. Crossley 210 $aBristol ;$aBuffalo $cMultilingual Matters$dc2012 215 $a1 online resource (196 p.) 225 1 $aSecond language acquisition ;$v64 300 $aDescription based upon print version of record. 311 $a1-84769-698-8 311 $a1-84769-697-X 320 $aIncludes bibliographical references. 327 $tFrontmatter --$tContents --$tContributors --$t1. The Detection-Based Approach: An Overview --$t2. Detecting L2 Writers? L1s on the Basis of Their Lexical Styles --$t3. Exploring the Role of n-Grams in L1 Identification --$t4. Detecting the First Language of Second Language Writers Using Automated Indices of Cohesion, Lexical Sophistication, Syntactic Complexity and Conceptual Knowledge --$t5. Error Patterns and Automatic L1 Identification --$t6. The Comparative and Combined Contributions of n-Grams, Coh-Metrix Indices and Error Types in the L1 Classification of Learner Texts --$t7. Detection-Based Approaches: Methods, Theories and Applications 330 $aRecent work has pointed to the need for a detection-based approach to transfer capable of discovering elusive crosslinguistic effects through the use of human judges and computer classifiers that can learn to predict learners? language backgrounds based on their patterns of language use. This book addresses that need. It details the nature of the detection-based approach, discusses how this approach fits into the overall scope of transfer research, and discusses the few previous studies that have laid the groundwork for this approach. The core of the book consists of five empirical studies that use computer classifiers to detect the native-language affiliations of texts written by foreign language learners of English. The results highlight combinations of language features that are the most reliable predictors of learners? language backgrounds. 410 0$aSecond language acquisition (Clevedon, England) ;$v64. 606 $aLanguage transfer (Language learning) 606 $aEnglish language$xRhetoric$xStudy and teaching 608 $aElectronic books. 615 0$aLanguage transfer (Language learning) 615 0$aEnglish language$xRhetoric$xStudy and teaching. 676 $a401/.93 701 $aJarvis$b Scott$f1966-$0908309 701 $aCrossley$b Scott A$01031467 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910452565403321 996 $aApproaching language transfer through text classification$92448873 997 $aUNINA