A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AQ4L95J4H" target="_blank" >RIV/00216208:11320/23:Q4L95J4H - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2079-9292/12/11/2357" target="_blank" >https://www.mdpi.com/2079-9292/12/11/2357</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/electronics12112357" target="_blank" >10.3390/electronics12112357</a>
Alternative languages
Result language
angličtina
Original language name
A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction
Original language description
"Experiments are conducted on four datasets: the Universal Dependencies 2.2, the Chinese Treebank 5.1, the English Penn Treebank 3.0 in 13 languages for syntactic dependency parsing, and the SemEval-2015 Task 18 dataset in three languages for semantic dependency parsing. The experimental results show that our best-performing model achieves a new state-of-the-art performance on most language sets of syntactic dependency and semantic dependency parsing. In addition, our model also has an advantage in running speed over the static graph-based learning model. The outstanding performance demonstrates the effectiveness of the proposed framework in structured prediction."
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"Electronics"
ISSN
1754-1786
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
2357
Pages from-to
1-2357
UT code for WoS article
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EID of the result in the Scopus database
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