"Buenos dias", "buenas noches" -- this was the first words in a foreign language I heard in my life, as a three-year old boy growing up in developing post-war Western Germany, where the first gastarbeiters had arrived from Spain. Fascinated by the strange sounds, I tried to get to know some more languages, the only opportunity being TV courses of English and French -- there was no foreign language education for pre-teen school children in Germany yet in those days. Read more
To find some answers Tim Machan explores the language's present and past, and looks ahead to its futures among the one and a half billion people who speak it. His search is fascinating and important, for definitions of English have influenced education and law in many countries and helped shape the identities of those who live in them.
This volume provides a new perspective on the evolution of the special language of medicine, based on the electronic corpus of Early Modern English Medical Texts, containing over two million words of medical writing from 1500 to 1700.
Approaching Language Transfer through Text Classification
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.