Improving Efficiency and Accuracy in English Translation Learning: Investigating a Semantic Analysis Correction Algorithm

Autor: Lingmei Cao, Junru Fu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Applied Artificial Intelligence, Vol 37, Iss 1 (2023)
Druh dokumentu: article
ISSN: 0883-9514
1087-6545
08839514
DOI: 10.1080/08839514.2023.2219945
Popis: Translation serves as a vital link in connecting individuals from diverse cultural backgrounds, assuming greater significance in the context of globalization. With the continued growth of international communication, the importance of effective translation in fostering mutual understanding, exploring new horizons, and building relationships cannot be overstated. However, the persistent language barrier necessitates improvements in translation accuracy and efficiency. In recent years, information technology has facilitated the development of innovative algorithms for English translation. This study aims to investigate correction algorithms for English translation learning, proposing a method that encompasses semantic word similarity calculations, a log-linear model construction, and the selection of appropriate translations using a dependency tree-to-string approach. Our research examines the performance of this system through comprehensive testing, showcasing noteworthy enhancements in the efficacy and precision of the translations in English. By addressing the users’ needs for English translation correction, this work contributes to bridging language gaps and fostering effective cross-cultural communication.
Databáze: Directory of Open Access Journals
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