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.

Scott Jarvis (Ph.D., Indiana University) holds the title of Professor in the Department of Linguistics at Ohio University, where his main research interests include crosslinguistic influence, cognitive linguistics, and research methods related to the investigation of language proficiency and the measurement of lexical diversity. His work in these areas has appeared in several authored and edited books, numerous book chapters and journal papers in the fields of second language acquisition and multilingualism. Professor Jarvis is also Associate Executive Director for the journal Language Learning.

Scott A. Crossley is an Assistant Professor at Georgia State University. His work involves the application of natural language processing theories and approaches for investigating second language acquisition, text readability, and writing proficiency. His current research interests include lexical proficiency, writing quality, and text coherence and processing.