Description

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.

Approaching Language Transfer through Text Classification: Explorations in the Detection-based Approach

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Hardback by Scott Jarvis , Scott A. Crossley

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Recent work has pointed to the need for a detection-based approach to transfer capable of discovering elusive crosslinguistic effects through... Read more

    Publisher: Channel View Publications Ltd
    Publication Date: 14/03/2012
    ISBN13: 9781847696984, 978-1847696984
    ISBN10: 1847696988

    Number of Pages: 200

    Non Fiction , Dictionaries, Reference & Language

    Description

    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.

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