  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.
