计算机应用与软件2016，Vol.33Issue(3)：284-287,4.DOI:10.3969/j.issn.1000-386x.2016.03.067
基于话题翻译模型的双语文本纠错
TOPICS TRANSLATION MODEL-BASED BILINGUAL TEXT ERRORS CORRECTION
摘要
Abstract
Along with the globalisation of information in recent years,multilingual mixing phenomena have become increasingly popular in social networks texts.It is quite common in Chinese texts that other languages are mixed.Since most of the existing natural language processing algorithm is the monolingual task-based,the multilingual mixed text can’t be well processed,therefore it is crucial to pre-process the text before carrying out other natural language processing tasks.For the lack of the corpus of bilingual alignment in network text semantic space,we proposed a topics translation model-based method,it calculates the probability of bilingual alignment of network text semantic space using the corpus in different semantic spaces,then incorporates neural network language model to translate the English in mixed network text to corresponding Chinese text.The experiment was set on a manual labelled test corpus.Experimental result indicated that through different comparative experiments it was proved that the proposed approach was effective and was able to improve translation accuracy.关键词
网络文本/话题翻译模型/神经网络语言模型Key words
Network text/Topics translation model/Neural network language model分类
信息技术与安全科学引用本文复制引用
陈欢,张奇..基于话题翻译模型的双语文本纠错[J].计算机应用与软件,2016,33(3):284-287,4.基金项目
陈欢，硕士，主研领域自然语言处理，机器学习。张奇，副教授。 （）
