Anti Dependency Distance Minimization in Short Sequences. A Graph Theoretic Approach

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.

Acknowledgments

We are very grateful to G. Jäger for his hospitality and rich discussions from many perspectives. We also thank D. Celinska-Kopczynska for helpful discussions on the problem of multiple comparisons and many suggestions to improve the article. The manuscript has benefited enormously from the comments of an anonymous reviewer. RFC is supported by the grant TIN2017-89244-R from MINECO (Ministerio de Economia, Industria y Competitividad) and the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). CGR has received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01, and a grant from Consellería de Cultura, Educación e Ordenación Universitaria to complement ERC grants).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the H2020 European Research Council [714150]; Ministerio de Economía, Industria y Competitividad, Gobierno de España [TIN2017-89244-R]; Ministerio de Economía, Industria y Competitividad, Gobierno de España (ES) [TIN2017-85160-C2-1-R]; Xunta de Galicia [ED431B 2017/01].