Research Article
Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 186 - Issue 64 |
| Published: January 2025 |
| Authors: Daniel Rojas Plata, No´E Alejandro Castro S´Anchez |
10.5120/ijca2025924438
|
Daniel Rojas Plata, No´E Alejandro Castro S´Anchez . Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications. International Journal of Computer Applications. 186, 64 (January 2025), 7-13. DOI=10.5120/ijca2025924438
@article{ 10.5120/ijca2025924438,
author = { Daniel Rojas Plata,No´E Alejandro Castro S´Anchez },
title = { Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 186 },
number = { 64 },
pages = { 7-13 },
doi = { 10.5120/ijca2025924438 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Daniel Rojas Plata
%A No´E Alejandro Castro S´Anchez
%T Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications%T
%J International Journal of Computer Applications
%V 186
%N 64
%P 7-13
%R 10.5120/ijca2025924438
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper analyzes the role of ontologies in improving translation systems. Statistical-based technologies were chosen as the analysis model, as they do not rely on grammar-based models or any other linguistic implementation. Since this architecture is based solely on probabilistic inferences, implementations like ontologies can help reduce ambiguity and thus improve the semantic and lexical aspects, which remain persistent issues. Specifically, this study reviews these problems and outlines guidelines for ontology development.
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