Integrating graph embedding and neural models for improving transition-based dependency parsing

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ADP3UGVQB" target="_blank" >RIV/00216208:11320/23:DP3UGVQB - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178157836&doi=10.1007%2fs00521-023-09223-3&partnerID=40&md5=d673340f56fbc637987bfe6b5b11b923" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178157836&doi=10.1007%2fs00521-023-09223-3&partnerID=40&md5=d673340f56fbc637987bfe6b5b11b923</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-023-09223-3" target="_blank" >10.1007/s00521-023-09223-3</a>

Result language
angličtina
Original language name
Integrating graph embedding and neural models for improving transition-based dependency parsing
Original language description
"This paper introduces an effective method for improving dependency parsing which is based on a graph embedding model. The model helps extract local and global connectivity patterns between tokens. This method allows neural network models to perform better on dependency parsing benchmarks. We propose to incorporate node embeddings trained by a graph embedding algorithm into a bidirectional recurrent neural network scheme. The new model outperforms a baseline reference using a state-of-the-art method on three dependency treebanks for both low-resource and high-resource natural languages, namely Indonesian, Vietnamese and English. We also show that the popular pretraining technique of BERT would not pick up on the same kind of signal as graph embeddings. The new parser together with all trained models is made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide in total. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature."
Czech name
—
Czech description
—

Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)