Dependency distance minimization predicts compression

TL;DR

This study tests whether dependency distance minimization predicts word length compression, confirming the prediction with a new scoring method when measuring in phonemes, and highlighting limitations of traditional scores.

Contribution

It introduces a new scoring method to test the link between dependency distance minimization and word length compression, confirming the prediction in phonemes but not syllables.

Findings

Confirmation of the prediction when measuring in phonemes

Failure to confirm when measuring in syllables

Highlights limitations of traditional dependency distance scores

Abstract

Dependency distance minimization (DDm) is a well-established principle of word order. It has been predicted theoretically that DDm implies compression, namely the minimization of word lengths. This is a second order prediction because it links a principle with another principle, rather than a principle and a manifestation as in a first order prediction. Here we test that second order prediction with a parallel collection of treebanks controlling for annotation style with Universal Dependencies and Surface-Syntactic Universal Dependencies. To test it, we use a recently introduced score that has many mathematical and statistical advantages with respect to the widely used sum of dependency distances. We find that the prediction is confirmed by the new score when word lengths are measured in phonemes, independently of the annotation style, but not when word lengths are measured in syllables. In contrast, one of the most widely used scores, i.e. the sum of dependency distances, fails to confirm that prediction, showing the weakness of raw dependency distances for research on word order. Finally, our findings expand the theory of natural communication by linking two distinct levels of organization, namely syntax (word order) and word internal structure.