Please use this identifier to cite or link to this item:
Title: A similarity measure method of Chinese sentence structures
Other Titles: 基于词类串的汉语句子结构相似度计算方法
Authors: Wang, R
Chi, Z 
Keywords: Artificial intelligence
Machine translation
Example-based machine translation
Chinese-English machine translation
Sentence similarity measure
Natural language processing
Issue Date: 2005
Publisher: 中国中文信息学会 ; 北京信息工程学院
Source: 中文信息学报 (Journal of Chinese information processing), 2005, v. 19, no. 1, p. 21-29 How to cite?
Journal: 中文信息学报 (Journal of Chinese information processing) 
Abstract: 句子相似度的衡量是基于实例机器翻译研究中最重要的一个内容。对于基于实例的汉英机器翻译研究 ,汉语句子相似度衡量的准确性 ,直接影响到最后翻译结果的输出。本文提出了一种汉语句子结构相似性的计算方法。该方法比较两个句子的词类信息串 ,进行最优匹配 ,得到一个结构相似性的值。在小句子集上的初步实验结果表明 ,该方法可行 ,有效 ,符合人的直观判断。
Example-based machine translation(EBMT)is an important branch of machine translation that has been studied extensively for about twenty years.So far,some progresses have been gained because of researchers’ hard work.Sentence similarity measure certainly is one of the most important problems addressed in EBMT.For EBMT from Chinese to English,the performance of similarity measure of Chinese sentences affects directly final translation result of an input sentence.In this paper,we proposed a similarity measure method of Chinese sentence structures for example-based Chinese to English machine translation.In this method,the algorithm performs the optimal matching between the word type sequences of two compared sentences.The preliminary experimental results show that the measure method works well when it is tested on a small dataset.
ISSN: 1003-0077
Rights: © 2005 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
© 2005 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research purposes.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
r29065.pdf268.38 kBAdobe PDFView/Open
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

Last Week
Last month
Citations as of Aug 14, 2018


Citations as of Aug 14, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.