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Volume 2, Issue 2
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Abstract

Abstract

In this article, we present the results of a corpus-based study where we explore whether it is possible to automatically single out different facets of text complexity in a general-purpose corpus. To this end, we use factor analysis as applied in Biber’s multi-dimensional analysis framework. We evaluate the results of the factor solution by correlating factor scores and readability scores to ascertain whether the selected factor solution matches the independent measurement of readability, which is a notion tightly linked to text complexity. The corpus used in the study is the Swedish national corpus, called or SUC. The SUC contains subject-based text varieties (e.g., hobby), press genres (e.g., editorials), and mixed categories (e.g., miscellaneous). We refer to them collectively as ‘registers’. Results show that it is indeed possible to elicit and interpret facets of text complexity using factor analysis despite some caveats. We propose a tentative text complexity profiling of the SUC registers.

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2020-08-13
2025-02-13

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/content/journals/10.1075/rs.19005.san
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Pinning down text complexity
Register Studies 2, 306 (2020); https://doi.org/10.1075/rs.19005.san
/content/journals/10.1075/rs.19005.san
/content/journals/10.1075/rs.19005.san
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  • Article Type: Research Article
Keyword(s): corpus-based analysis; correlation tests; factor analysis; Multi-Dimensional Analysis (MDA); multivariate statistics; readability; text complexity

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