Anti Dependency Distance Minimization in Short Sequences. A Graph Theoretic Approach. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Ferrer-i-Cancho, Ramon; Gómez-Rodríguez, Carlos
• Is Part Of: Journal of quantitative linguistics. Volume 28:Issue 1(2021)
• Page Start: 50
• Page End: 76
• Publication Date: 2021-01-02
• Subjects: Linguistics -- Statistical methods -- Periodicals; 410.151
• DOI: 10.1080/09296174.2019.1645547
• Languages: English
• ISSNs: 0929-6174

Abstract

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.