Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10427011" target="_blank" >RIV/00216208:11320/20:10427011 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/2020.emnlp-main.329" target="_blank" >https://www.aclweb.org/anthology/2020.emnlp-main.329</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
Original language description
Across languages, multiple consecutive adjectives modifying a noun (e.g. “the big red dog”) follow certain unmarked ordering rules. While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data. We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different. We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies.
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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů