Recognition and Segmentation of English Long and Short Sentences Based on Machine Translation

KOVALEN (Jurnal Riset Kimia)

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Field Value
 
Title Recognition and Segmentation of English Long and Short Sentences Based on Machine Translation
 
Creator Zhang, Tiehu; School of Foreign Languages, Xi'an Aeronautical University
 
Subject machine translation, long sentence, regular match, error-driven method
 
Description With the advent of the information age, long sentences which include many words and have more complex structures.. The translation of long sentences in English-Chinese machine translation has always been the focus of research. In this study, 400 long sentences were randomly selected from NTCIR-9 patent corpus for testing the recognition and segmentation effects of regular match method and error-driven method, and the accuracy rate of the translation was compared on Baidu Online Translation Platform. The results demonstrated that the regular matching method was effective in recognizing and segmenting long sentences, nevertheless there were many defects; the error-driven method was more effective in recognizing and segmenting long sentences; the former increased by 4.8% of the BLEU value of the translated text on Baidu Online Translation Platform and the latter increased by 12.1%, which showed that the error-driven method was more effective in machine translation.
 
Publisher International Association of Online Engineering (IAOE)
 
Contributor
 
Date 2020-01-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://online-journals.org/index.php/i-jet/article/view/10182
10.3991/ijet.v14i19.10182
 
Source International Journal of Emerging Technologies in Learning (iJET); Vol 15, No 01 (2020); pp. 152-162
1863-0383
 
Language eng
 
Relation https://online-journals.org/index.php/i-jet/article/view/10182/6331
 
Rights Copyright (c) 2020 Tiehu Zhang
 

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