978 - 1 - 107 - 04119 - 6 - The Cambridge Handbook of Learner Corpus

Written and spoken data produced by learners has always been a key resource for the study of second language acquisition (SLA). However, for a long time the data used was rather artifi cial, i.e. resulting from highly controlled language tasks , and therefore not necessarily a refl ection of what learners do in more natural communication contexts. In addition, the data samples were usually quite small, often involving no more than a handful of learners, and therefore raised concerns in terms of representativeness. The combined wish to address these two issues and produce more learner-aware/learner-focused pedagogical tools prompted the emergence of learner corpora, which can be defi ned as electronic collections of natural or near-natural data produced by foreign or second language (L2) learners and assembled according to explicit design criteria. Learner corpora gave rise to a fl urry of studies, which have come to be grouped under the umbrella term of ‘learner corpus research’ (LCR). This new research strand emerged in the late 1980s as an offshoot of corpus linguistics, a fi eld which had shown great potential in investigating a wide range of native language varieties (diachronic, stylistic, regional) but had neglected the non-native varieties. In the case of English, by far the most widely investigated language at the time, this was hardly justifi ed in view of the fact that the number of non-native speakers far outnumbers that of native speakers. Having access to electronic collections of L2 data presents two signifi cant advantages. First, as these collections are usually quite large and are collected from a great number of learners, they are arguably more representative than smaller data samples involving a limited number of learners. Second being in electronic format, the data can be analysed with a whole battery of software tools that greatly speed up the analysis and enable a wide range of investigations that either cannot be performed manually at all or only at huge cost in terms of human resources. Part-of-speech taggers , for example, assign to each word in a learner