Analysis of Machine Translation and Post-Translation Editing Ability Using Semantic Information Entropy Technology.
Large-scale corpus application has presented MT with new opportunities as well as challenges in recent years. This study investigates MT and post-translation editing capability using AI technology. The grammar rules of the target language are first examined. Then, a significant amount of data on sem...
| Publicado en: | Journal of Environmental & Public Health pp. 1 - 11 |
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| Autor principal: | |
| Formato: | research Journal Article |
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Wiley-Blackwell
8/18/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158604353&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158604353 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16879805 9034 jtl: Journal of Environmental & Public Health issn: 16879805 maglogo: N pubinfo: dt: 8/18/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158604353 158604353 NLM36034629 158604353 10.1155/2022/5932044 NLM36034629 158604353 ppf: 1 ppct: 10 formats: tig: atl: Analysis of Machine Translation and Post-Translation Editing Ability Using Semantic Information Entropy Technology. aug: au: Zou, Siyu affil: School of Foreign Languages, Nanchang Institute of Technology, Nanchang 330000, China sug: subj: Semantics Language Technology Physics Clinical Assessment Tools Ferrans and Powers Quality of Life Index ab: Large-scale corpus application has presented MT with new opportunities as well as challenges in recent years. This study investigates MT and post-translation editing capability using AI technology. The grammar rules of the target language are first examined. Then, a significant amount of data on semantic information entropy are projected, and the semantic Gaussian marginal rectangular window function is obtained. The semantic correlation factors of words are added to the text information entropy and information gain, and the nonlinear spectral properties of adaptive matching semantics are obtained. In this way, it corrects the significant flaw in the way semantic features are extracted using conventional techniques. In order to speed up MT and enhance translation quality, this study proposes automatic post-translation editing to filter those common MT errors that occur frequently and regularly. According to the experimental findings, word translation and segmentation accuracy can both reach 95.27 and 93.12 percent, respectively. In terms of language translation, this approach is accurate and trustworthy. I hope it will serve as a useful source for subsequent research. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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