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Representativeness in Corpus Design

BIBER, D.·1993·Literary and Linguistic Computing

Abstract

The present paper addresses a number of issues related to achieving ‘representativeness’ in linguistic corpus design, including: discussion of what it means to ‘represent’ a language, definition of the target population, stratified versus proportional sampling of a language, sampling within texts, and issues relating to the required sample size (number of texts) of a corpus. The paper distinguishes among various ways that linguistic features can be distributed within and across texts; it analyses the distributions of several particular features, and it discusses the implications of these distributions for corpus design. The paper argues that theoretical research should be prior in corpus design, to identify the situational parameters that distinguish among texts in a speech community, and to identify the types of linguistic features that will be analysed in the corpus. These theoretical considerations should be complemented by empirical investigations of linguistic variation in a pilot corpus of texts, as a basis for specific sampling decisions. The actual construction of a corpus would then proceed in cycles: the original design based on theoretical and pilot-study analyses, followed by collection of texts, followed by further empirical investigations of linguistic variation and revision of the design.

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Metadata

Authors
BIBER, D.
Venue
Literary and Linguistic Computing
Published
1993-10-01
Volume
8
Issue
4
Pages
243-257
ISSN
  • 0268-1145 (ISSN-L)
  • 1477-4615
OpenAlex
W2148259819
Cited by
1,269
References
0
FWCI
1.48
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Representativeness in Corpus Design. https://app.cochat.ai/papers/10.1093/llc/8.4.243
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BIBER, D. (1993). Representativeness in Corpus Design. Literary and Linguistic Computing. https://doi.org/10.1093/llc/8.4.243

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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.