State-of-the-art Italian dependency parsers based on neural and ensemble systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427064" target="_blank" >RIV/00216208:11320/19:10427064 - isvavai.cz</a>
Result on the web
<a href="http://journals.openedition.org/ijcol/454" target="_blank" >http://journals.openedition.org/ijcol/454</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
State-of-the-art Italian dependency parsers based on neural and ensemble systems
Original language description
In this paper we present a work which aims to test the most advanced, state-of-the-art syntactic dependency parsers based on deep neural networks (DNN) on Italian. We made a large set of experiments by using two Italian treebanks containing different text types downloaded from the Universal Dependencies project and propose a new solution based on ensemble systems. We implemented the proposed ensemble solutions by testing different techniques described in literature, obtaining very good parsing results, well above the state of the art for Italian.
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