A Semantics-Aware Head-Driven Approach for Multilingual Dependency Parsing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3ARYEULSIT" target="_blank" >RIV/00216208:11320/26:RYEULSIT - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3601225" target="_blank" >http://dx.doi.org/10.1109/ACCESS.2025.3601225</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3601225" target="_blank" >10.1109/ACCESS.2025.3601225</a>
Alternative languages
Result language
angličtina
Original language name
A Semantics-Aware Head-Driven Approach for Multilingual Dependency Parsing
Original language description
Dependency parsing is essential for language modeling because it offers a structured understanding of the syntactic relationships between words in a sentence. While recent advancements in large language models have greatly advanced the field of natural language processing, dependency parsing remains highly relevant for several key reasons. This paper introduces an effective method for improving dependency parsing which is based on a semantics-aware token embedding model. We propose to incorporate the ConceptNet embeddings which are trained by a retrofitting algorithm into a bidirectional recurrent neural network. The new model outperforms a strong baseline that employs a state-of-the-art method across three dependency treebanks, covering both low-resource and high-resource languages—Indonesian, Vietnamese, and English—achieving an improvement of approximately 1.21% in labeled attachment score. We also show that this method outperforms the popular transformer-based BERT model in capturing syntactic dependency between tokens. The new parser together with all trained models are 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. © 2013 IEEE.
Czech name
—
Czech description
—
Classification
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)
Result continuities
Project
—
Continuities
—
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
—
Volume of the periodical
13
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
148456-148466
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-105013990333