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Characterizing linguistic structure with mutual information.

Emmanuel M Pothos1, Patrick Juola

  • 1Department of Psychology, Swansea University, Swansea, UK. e.m.pothos@swansea.ac.uk

British Journal of Psychology (London, England : 1953)
|April 26, 2007
PubMed
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Mutual information (MI) reveals that word dependencies in text span only five words across 25 languages. This linguistic statistical structure may link to short-term memory, with MI aiding analysis.

Area of Science:

  • Computational linguistics
  • Cognitive science
  • Information theory

Background:

  • Linguistic structure is often analyzed using statistical methods.
  • Mutual information (MI) quantifies the statistical dependency between variables.
  • Understanding word co-occurrence patterns can reveal language properties.

Purpose of the Study:

  • To characterize linguistic statistical structure using mutual information (MI).
  • To determine the effective range of word dependencies in written text.
  • To explore cross-linguistic similarities in MI profiles.

Main Methods:

  • Computed MI between words in 595 text samples across 25 languages.
  • Analyzed the decay of MI with increasing word distance.
  • Clustered languages based on the similarity of their MI profiles.

Related Experiment Videos

Main Results:

  • MI-based word dependencies were found to extend up to a maximum of five words.
  • Distinct MI profiles emerged for different languages.
  • Language clustering based on MI similarity revealed linguistic relationships.

Conclusions:

  • Linguistic statistical structure, as measured by MI, is short-range, potentially linked to cognitive constraints like short-term memory.
  • MI provides a valuable tool for quantifying and comparing linguistic structures across languages.
  • Further research can explore the cognitive underpinnings and applications of MI in linguistics.