Part of
Corpus Methods for Semantics: Quantitative studies in polysemy and synonymyEdited by Dylan Glynn and Justyna A. Robinson
[Human Cognitive Processing 43] 2014
► pp. 443–485
Edited by Dylan Glynn and Justyna A. Robinson
[Human Cognitive Processing 43] 2014
► pp. 443–485
Correspondence analysis
Exploring data and identifying patterns
Correspondence analysis is an exploratory technique for complex categorical data, typical of corpus-driven research. It identifies patterns of association and disassociation in those data. For instance, it can map the correlations between different uses of a linguistic form and its various social and/or morpho-syntactic contexts. The technique presents its results in the form of a two-dimensional plot, which visualises these relationships in an intuitive manner. These plots offer rich representations of the relations between different facets of complex data. Using R, this chapter explains how the technique works and offers a step-by-step explanation of its application and the interpretation of its results. The technique is also compared to the better-known and comparable cluster analysis.
Keywords: categorical data, cluster analysis, exploratory statistics, R
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