The correspondence analysis platform for uncovering deep structure in data and information

Research output: Contribution to journalArticle

12 Citations (Scopus)


We study two aspects of information semantics: (i) the collection of all relationships, (ii) tracking and spotting anomaly and change. The first is implemented by endowing all relevant information spaces with a Euclidean metric in a common projected space. The second is modelled by an induced ultrametric. A very general way to achieve a Euclidean embedding of different information spaces based on cross-tabulation counts (and from other input data formats) is provided by correspondence analysis. From there, the induced ultrametric that we are particularly interested in takes a sequential - e.g. temporal - ordering of the data into account. We employ such a perspective to look at narrative, 'the flow of thought and the flow of language' (Chafe). In application to policy decision making, we show how we can focus analysis in a small number of dimensions.

Original languageEnglish
Pages (from-to)304-315
Number of pages12
JournalComputer Journal
Issue number3
Publication statusPublished - 1 Mar 2010
Externally publishedYes


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