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Corpus Linguistics at Work
Abstract
The book offers a combined discussion of the main theoretical, methodological and application issues related to corpus work. Thus, starting from the definition of what is a corpus and why reading a corpus calls for a different methodology from reading a text, the underlying assumptions behind corpus work are discussed. The two main approaches to corpus work are discussed as the “corpus-based” and the “corpus-driven” approach and the theoretical positions underlying them explored in detail. The book adopts and exemplifies the parameters of the corpus-driven approach and posits a new unit of linguistic description defined systematically in the light of corpus evidence. The applications where the corpus-driven approach is exemplified are language teaching and contrastive linguistics. Alternating between practical examples and theoretical evaluation, the reader is led step-by-step to a detailed understanding of the issues involved in corpus work and, at the same time, tempted to explore for himself some of the major applications where a corpus-driven methodology can reveal unprecedented insights into linguistic patterning.
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Citations
Cited by
Text, Context, Pretext: Critical Issues in Discourse Analysis
Widdowson, H. G. (2004) · doi:10.1002/9780470758427 · Cited by 526
Corpus-Assisted Discourse Studies
Gillings, M., Mautner, G., & Baker, P. (2023) · doi:10.1017/9781009168144 · Cited by 146
Metadata
- Authors
- Tognini-Bonelli, E.
- Venue
- Studies in Corpus Linguistics
- Publisher
- John Benjamins Publishing Company
- Published
- 2001-04-11
- ISSN
- 1388-0373 (ISSN-L)
- OpenAlex
- W1982878818
- Cited by
- 1,756
- References
- 0
- FWCI
- 98.59
- Type
- book
- Language
- en
- Open access
- closed
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- Tognini-Bonelli, E. (2001). Corpus Linguistics at Work. Studies in Corpus Linguistics. https://doi.org/10.1075/scl.6
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
