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SECTION B: FORMULAIC LANGUAGE AND PEDAGOGICAL ISSUES
It is now widely accepted that language is largely formulaic in nature, and that phraseological competence is an important part of nativelike, fluent, and idiomatic language use (Hoey, 2005; Pawley & Syder, 1983; Wray & Perkins, 2000). It is also becoming increasingly apparent that phraseology plays an important role in foreign language learning and teaching (Ellis, 2003; Howarth, 1998; Lewis, 1993; Nattinger & DeCarrico, 1992). Just as "phraseology binds words, grammar, semantics, and social usage" (Ellis, 2008, p. 5), the use of phraseological units such as collocations, phrasal verbs, compounds, and idioms may impact positively or negatively on the three dimensions of language proficiency--complexity, accuracy, and fluency (Housen & Kuiken, 2009). Osborne (2008), for example, identified some phraseological effects that trigger some categories of persistent learner errors such as the omission of third-person -s or the pluralization of adjectives.
The last few years have seen a proliferation of phraseological studies based on learner corpora, which can be roughly defined as electronic collections of texts produced by foreign or second language (L2) learners. As learner language is highly heterogeneous--there are many types of learners and learning situations--learner corpus data are compiled on the basis of strict design criteria, pertaining to the learner (e.g., age, gender, mother-tongue background, number of years spent studying the foreign language, or time spent in a country where the foreign language is spoken) and the task (e.g., medium, topic, timing, or the use of reference tools). Learner corpora have two distinguishing characteristics that make them an ideal source of data to study the learner phrasicon, that is, the whole set of formulaic sequences in learner language: (a) They contain continuous stretches of oral or written discourse rather than decontextualized words, phrases or sentences, and (b) they typically include data resulting from pedagogical tasks that allow learners to choose their own wording (e.g., essay writing, learner interviews; cf. Granger, 2008; Granger, in press) rather than being requested to produce a particular word or structure. In addition, as the data are in electronic format, analysts can use a whole range of automated methods and tools to identify and analyze formulaic sequences in learner language.
This chapter aims to provide a...