The aim of the present research is to identify and classify, through computational methods based on the usage of corpora and statistical tools, the academic spoken Italian lexicon. The academic lexicon is tightly linked to the learning and didactic activities with which students daily come in contact with during lessons, exams, conferences and so on. A right and immediate decoding of some lexical units such as contesto (context), teoria (theory), approccio (approach), or some articulate expressions such as introdurre un concetto (to introduce a concept) or trattare un argomento (to deal with a topic), is a fundamental step to proceed successfully throughout the academic career and then to get into the working world. This is concerned first of all with L2 students, that have to discover a different social and linguistic context. Furthermore the lexical academic competence includes both production and comprehension, because of the usage that students have to do of the academic language; to learn vocabulary in another language they have to use it, so the productive phase constitutes a key component in the achievement of the academic purposes. The immediate access to the meaning of a word allows students to focus on the content of what they try to explain, rather than on the way to explain that concept. The corpus developed in this research is an opportunity given to foreign students to exercise on lexicon and to increase their lexical performance. But this is also a useful tool for teachers, who can prepare specific course material focused on this particular linguistic register. The contents of the present research are therefore based on the real learning and linguistic needs registered among non-native students, who want to become more and more aware of knowing how to do and how to communicate something in an academic daily situation.

Linguistic and computational tools in support of non native Italian speaking students: the development of the Academic Spoken Italian Corpus

Peppoloni D
2012

Abstract

The aim of the present research is to identify and classify, through computational methods based on the usage of corpora and statistical tools, the academic spoken Italian lexicon. The academic lexicon is tightly linked to the learning and didactic activities with which students daily come in contact with during lessons, exams, conferences and so on. A right and immediate decoding of some lexical units such as contesto (context), teoria (theory), approccio (approach), or some articulate expressions such as introdurre un concetto (to introduce a concept) or trattare un argomento (to deal with a topic), is a fundamental step to proceed successfully throughout the academic career and then to get into the working world. This is concerned first of all with L2 students, that have to discover a different social and linguistic context. Furthermore the lexical academic competence includes both production and comprehension, because of the usage that students have to do of the academic language; to learn vocabulary in another language they have to use it, so the productive phase constitutes a key component in the achievement of the academic purposes. The immediate access to the meaning of a word allows students to focus on the content of what they try to explain, rather than on the way to explain that concept. The corpus developed in this research is an opportunity given to foreign students to exercise on lexicon and to increase their lexical performance. But this is also a useful tool for teachers, who can prepare specific course material focused on this particular linguistic register. The contents of the present research are therefore based on the real learning and linguistic needs registered among non-native students, who want to become more and more aware of knowing how to do and how to communicate something in an academic daily situation.
978-84-8409-593-4
corpus linguistics; academic spoken language; second language acquisition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12071/11276
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