What can we learn from natural and artificial dependency trees - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

What can we learn from natural and artificial dependency trees

Chunxiao Yan

Résumé

This paper is centered around two main contributions : the first one consists in introducing several procedures for generating random dependency trees with constraints; we later use these artificial trees to compare their properties with the properties of natural trees (i.e trees extracted from treebanks) and analyze the relationships between these properties in natural and artificial settings in order to find out which relationships are formally constrained and which are linguistically motivated. We take into consideration five metrics: tree length, height, maximum arity, mean dependency distance and mean flux weight, and also look into the distribution of local configurations of nodes. This analysis is based on UD treebanks (version 2.3, Nivre et al. 2018) for four languages: Chinese, English, French and Ja-panese.
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Dates et versions

hal-02416656 , version 1 (17-12-2019)

Identifiants

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Marine Courtin, Chunxiao Yan. What can we learn from natural and artificial dependency trees. Quasy 2019, Quantitative Syntax, Syntaxfest, Aug 2019, Paris, France. pp.125-135, ⟨10.18653/v1/W19-7915⟩. ⟨hal-02416656⟩
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