What can we learn from natural and artificial dependency trees
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427159" target="_blank" >RIV/00216208:11320/19:10427159 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/W19-7915" target="_blank" >https://www.aclweb.org/anthology/W19-7915</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
What can we learn from natural and artificial dependency trees
Original language description
This paper is centered around two main contributions : the first one consists in introducing severalprocedures for generating random dependency trees with constraints; we later use these artificial treesto 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 findout which relationships are formally constrained and which are linguistically motivated. We take intoconsideration five metrics: tree length, height, maximum arity, mean dependency distance and meanflux weight, and also look into the distribution of local configurations of nodes. This analysis is basedon UD treebanks (version 2.3, Nivre et al. 2018) for four languages: Chinese, English, French and Japanese.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů