Word is the most active factor in the research of culture connotations and it is essential in understanding the culture of target language. This article not only attempts to analyze non-equivalence in semantic and cul...Word is the most active factor in the research of culture connotations and it is essential in understanding the culture of target language. This article not only attempts to analyze non-equivalence in semantic and cultural connotation of words between Chinese and English words from cross-cultural perspective, but also highlights the profound understanding of the relation of culture, and language is the best way to avoid cross-cultural barriers and obstacles. Finally, by elaborating the loan words between two different cultures, the paper maintains the cultural integration out of respect, appreciation and acceptance is the embodiment of human social and cultural development of human society and the vitality of the cultural changes.展开更多
Parallel corpus is of great importance to machine translation, and automatic sentence alignment is the first step towards its processing. This paper puts forward a bilingual dictionary based sentence alignment method ...Parallel corpus is of great importance to machine translation, and automatic sentence alignment is the first step towards its processing. This paper puts forward a bilingual dictionary based sentence alignment method for Chinese English parallel corpus, which differs from previous length based algorithm in its knowledge-rich approach. Experimental result shows that this method produces over 93% accuracy with usual English-Chinese dictionaries whose translations cover 31 88%～47 90% of the corpus.展开更多
In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of...In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.展开更多
文摘Word is the most active factor in the research of culture connotations and it is essential in understanding the culture of target language. This article not only attempts to analyze non-equivalence in semantic and cultural connotation of words between Chinese and English words from cross-cultural perspective, but also highlights the profound understanding of the relation of culture, and language is the best way to avoid cross-cultural barriers and obstacles. Finally, by elaborating the loan words between two different cultures, the paper maintains the cultural integration out of respect, appreciation and acceptance is the embodiment of human social and cultural development of human society and the vitality of the cultural changes.
文摘Parallel corpus is of great importance to machine translation, and automatic sentence alignment is the first step towards its processing. This paper puts forward a bilingual dictionary based sentence alignment method for Chinese English parallel corpus, which differs from previous length based algorithm in its knowledge-rich approach. Experimental result shows that this method produces over 93% accuracy with usual English-Chinese dictionaries whose translations cover 31 88%～47 90% of the corpus.
文摘In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.
