English Machine Translation System Based on Semantic Selection and Information Features
- DOI
- 10.2991/978-94-6463-040-4_145How to use a DOI?
- Keywords
- computer technology; semantic selection; artificial intelligence; information analysis; English translation
- Abstract
The development of machine translation system has not yet fully realized automatic and high-quality, because human language is complex, and there are certain limitations in understanding language rules. Therefore, in the research of machine translation, it not only is limited to the syntactic and semantic analysis of a single sentence, but also requires an in-depth analysis of the internal laws of the language, including: sentence groups, paragraphs, chapters, and contextual information inherent in genres. The English machine translation system based on semantic selection and information features conducts in-depth analysis of the source language sentences according to the semantic unit library, and completes the translation in combination with the target language sentences. A multi-language machine translation system based on semantic language needs a unified multi-natural language machine translation software and a multi-language unified semantic unit library (the basis for the establishment of the machine translation system).
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Pengyan Lu PY - 2022 DA - 2022/12/27 TI - English Machine Translation System Based on Semantic Selection and Information Features BT - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022) PB - Atlantis Press SP - 963 EP - 967 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-040-4_145 DO - 10.2991/978-94-6463-040-4_145 ID - Lu2022 ER -