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Article published In: International Journal of Corpus Linguistics
Vol. 23:2 (2018) ► pp.183–215

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Multi-unit association measures

Moving beyond pairs of words

 | University of Canterbury
Published online: 5 October 2018
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https://doi.org/10.1075/ijcl.16098.dun
Abstract
This paper formulates and evaluates a series of multi-unit measures of directional association, building on the pairwise ΔP measure, that are able to quantify association in sequences of varying length and type of representation. Multi-unit measures face an additional segmentation problem: once the implicit length constraint of pairwise measures is abandoned, association measures must also identify the borders of meaningful sequences. This paper takes a vector-based approach to the segmentation problem by using 18 unique measures to describe different aspects of multi-unit association. An examination of these measures across eight languages shows that they are stable across languages and that each provides a unique rank of associated sequences. Taken together, these measures expand corpus-based approaches to association by generalizing across varying lengths and types of representation.
Article outline
  • 1.Introduction
  • 2.Direction of association and sequence length
  • 3.Data and methodology
  • 4.Analysis: Formulating multi-unit association measures
    • 4.1Mean ΔP and Sum ΔP
    • 4.2Minimum ΔP
    • 4.3Reduced ΔP
    • 4.4Divided ΔP
    • 4.5End-point ΔP
    • 4.6Changed ΔP
    • 4.7Summarizing the association measures
  • 5.Discussion: Empirical analysis of association measures
    • 5.1Relations between directions and measures
    • 5.2Stability across languages and representation types
  • 6.Using the measures together
  • 7.Conclusions
  • Acknowledgements
  • References
References (26)
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