This book is largely based on the presentations made during ECOLINGUA DAY, an event organised at the University of Trieste in order to hear papers illustrating the results of a number of the varoius research sub-projects comprising the PRIN project ECOLINGUA, financed by the Italian Ministry for the University, and brought to conclusion by the five university units involved (The Catholic University of Milan, the University of Padua, the University of Pavia 1 & 2, the University of Trieste). Contributions range from corpus-based studies relating to European Union documents and to films, to surveys into the language used in subtitles, authorial presence in psychology articles, academic pratices in linguistics and grammatical usage. Language learning and the teaching of phonetics through corpora are also included.
Christopher Taylor è professore di Lingua e Traduzione (Inglese)
presso la Facoltà di Scienze della Formazione dell’Università degli Studi
di Trieste. È autore di numerosi articoli e libri, compreso Language to
Language, CUP 1998 e Look who’s talking in Massed Medias, LED, 1999. Il suo campo di ricerca principale è quello dell’analisi del linguaggio del
film e della traduzione dei testi multimodali. È attualmente direttore del
Centro Linguistico d’Ateneo e Presidente dell’AICLU (Associazione
nazionale dei centri linguistici universitari).
Browsing Ecolingua: the Role of E-corpora in Translation and Language Learning by Subject "corpus analysis"
Most scholars agree on considering corpora as a valuable source of linguistic information for native and non-native speakers alike. Few researchers, however, have dealt with and systematically analysed the objective difficulties encountered by students while trying to exploit corpus data. The current paper describes a quantitative study of corpus consultation by learners and aims to establish whether different corpus analysis tasks can be considered to have different degrees of intrinsic difficulty. To this end, 26 corpus project work assignments produced by two different groups of students were assessed and tagged according to specific parameters that reflect the skills needed in corpus analysis. The data were analysed applying both parametric (ANOVA) and non parametric tests (Mann-Whitney U-test), which showed that, despite clear individual and teaching/learning environment differences between the two groups of students, the students’ results in most of the tasks were due to different levels of intrinsic difficulty. This led to the creation of a General Difficulty List of Corpus Analysis Tasks.