Parts of speech conversion refers to transforming the certain words of the source language into another category words of the target language, which is one of the common methods and techniques used in the translation ...Parts of speech conversion refers to transforming the certain words of the source language into another category words of the target language, which is one of the common methods and techniques used in the translation of English and Chinese languages. This paper starts from a relatively significant difference in English and Chinese language— the tendency of static words in English in contrast to that of dynamic words in Chinese, to explore the theoretical basis of transference in parts of speech in English and Chinese language translation. Combining with a large number of examples, the author puts forward some skills on transformation of parts of speech in English-Chinese translation to guide translation practice. The study found that the theoretical basis of the conversion in English and Chinese mostly including:1) there is a boundary ambiguity between lexical category;2)English SV/SVO structure is rigorous, which leads to expressing the dynamic meaning by means of using other lexical category; 3) the development of social culture not only makes new words continue to increase rapidly, but also gives many used words with new meanings; 4)it is acknowledged in Lexical morphology that English words which come from derivation are in larger number and derivations can make the word class of the corresponding original words either the same or different. The dynamic and specific features of Chinese make it more use of verbs in language use. Thus, in the process of translation, appropriate parts of speech conversion can make the translation more in line with their own habits of expression.展开更多
Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translat...Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translated texts on Neijing. Due to the diversity of editions and different understanding, the translating styles and contents are widely different. This study will focus on the different translating styles on culture-specific lexicon、figure of speech and four-Chinese-character structures in Neijin.展开更多
Hidden Markov Model(HMM) is a main solution to ambiguities in Chinese segmentation and POS (part-of-speech) tagging. While most previous works for HMM-based Chinese segmentation and POS tagging consult POS information...Hidden Markov Model(HMM) is a main solution to ambiguities in Chinese segmentation and POS (part-of-speech) tagging. While most previous works for HMM-based Chinese segmentation and POS tagging consult POS information in contexts, they do not utilize lexical information which is crucial for resolving certain morphological ambiguity. This paper proposes a method which incorporates lexical information and wider context information into HMM. Model induction and related smoothing technique are presented in detail. Experiments indicate that this technique improves the segmentation and tagging accuracy by nearly 1%.展开更多
文摘Parts of speech conversion refers to transforming the certain words of the source language into another category words of the target language, which is one of the common methods and techniques used in the translation of English and Chinese languages. This paper starts from a relatively significant difference in English and Chinese language— the tendency of static words in English in contrast to that of dynamic words in Chinese, to explore the theoretical basis of transference in parts of speech in English and Chinese language translation. Combining with a large number of examples, the author puts forward some skills on transformation of parts of speech in English-Chinese translation to guide translation practice. The study found that the theoretical basis of the conversion in English and Chinese mostly including:1) there is a boundary ambiguity between lexical category;2)English SV/SVO structure is rigorous, which leads to expressing the dynamic meaning by means of using other lexical category; 3) the development of social culture not only makes new words continue to increase rapidly, but also gives many used words with new meanings; 4)it is acknowledged in Lexical morphology that English words which come from derivation are in larger number and derivations can make the word class of the corresponding original words either the same or different. The dynamic and specific features of Chinese make it more use of verbs in language use. Thus, in the process of translation, appropriate parts of speech conversion can make the translation more in line with their own habits of expression.
文摘Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translated texts on Neijing. Due to the diversity of editions and different understanding, the translating styles and contents are widely different. This study will focus on the different translating styles on culture-specific lexicon、figure of speech and four-Chinese-character structures in Neijin.
基金国家高技术研究发展计划(863计划)，the National Natural Science Foundation of China
文摘Hidden Markov Model(HMM) is a main solution to ambiguities in Chinese segmentation and POS (part-of-speech) tagging. While most previous works for HMM-based Chinese segmentation and POS tagging consult POS information in contexts, they do not utilize lexical information which is crucial for resolving certain morphological ambiguity. This paper proposes a method which incorporates lexical information and wider context information into HMM. Model induction and related smoothing technique are presented in detail. Experiments indicate that this technique improves the segmentation and tagging accuracy by nearly 1%.
