Collocation Dictionaries as Inductive Learning Resources in Data-Driven Learning - An Analysis and Evaluation

Bibliographic Data

ID12205205
AuthorsIain McGee (0000-0003-0919-4270, University of Nizwa, corresponding author)
Year2012
Volume25
Issue3
Pages319-361
Publication date2012-03-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInternational Journal of Lexicography (JOURNAL)
Journal identifiersISSN: 0950-3846 • E-ISSN: 1477-4577
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1093/ijl/ecr040
OpenAlexW2325077119
LanguageEN
Citations received4
References cited21

Advocacy of corpus-based data-driven learning (DDL) in foreign language learning classrooms has grown in recent years, though DDL has not become particularly widespread in practice. In the same period of time, there has been considerable publishing activity of corpus-based collocation dictionaries; however, the possible role that collocation dictionaries may play in DDL has not, to date, been considered. This paper seeks to analyse what contribution four English monolingual collocation dictionaries might make to ‘soft’ DDL inductive learning activities in the classroom. The paper provides a description and comparison of the dictionaries, and examines and compares the data provided by concordance lines for a series of DDL questions, with data from the collocation dictionaries. The paper ends by making a number of recommendations for the inductive use of the four collocation dictionaries, and suggests ways in which the dictionaries might be adapted to such usage

Collocation (remote sensing · Linguistics · Machine learning · Natural language processing · Computer Science · Lexicography and Language Studies · Natural Language Processing Techniques · Second Language Acquisition and Learning · Artificial Intelligence

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