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arXiv · 2104.01297

Multi-Unit Directional Measures of Association: Moving Beyond Pairs of Words

Jonathan Dunn
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Abstract

This paper formulates and evaluates a series of multi-unit measures of directional association, building on the pairwise {\Delta}P 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.

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Jonathan Dunn. 2021-04-03. Multi-Unit Directional Measures of Association: Moving Beyond Pairs of Words. https://doi.org/10.1075/ijcl.16098.dun

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