# Anti dependency distance minimization in short sequences. A graph theoretic approach

## TL;DR

This paper investigates the phenomenon where short sequences in language sometimes violate the dependency distance minimization principle, using a graph-theoretic approach and a binomial test to analyze syntactic structures across languages.

## Contribution

It introduces a novel binomial test to detect anti-dependency distance minimization in short sequences and links this phenomenon to star tree structures in syntax.

## Key findings

- Anti-DDm observed in some languages' short sequences
- Star trees are associated with anti-DDm patterns
- Method provides a new way to analyze syntactic dependency structures

## Abstract

Dependency distance minimization (DDm) is a word order principle favouring the placement of syntactically related words close to each other in sentences. Massive evidence of the principle has been reported for more than a decade with the help of syntactic dependency treebanks where long sentences abound. However, it has been predicted theoretically that the principle is more likely to be beaten in short sequences by the principle of surprisal minimization (predictability maximization). Here we introduce a simple binomial test to verify such a hypothesis. In short sentences, we find anti-DDm for some languages from different families. Our analysis of the syntactic dependency structures suggests that anti-DDm is produced by star trees.