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

arXiv: https://arxiv.org/abs/2104.01297 · HTML: https://mlsanity.com/paper/2104.01297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 3 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging](https://mlsanity.com/paper/1412.4021/) (2014)

## Built on by (most cited first, 1 of 1)

- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)

## Abstract (arXiv)

This paper formulates and evaluates a series of multi-unit measures of directional association, building on the pairwise DeltaP 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.