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Toyo Yoshizaki 《Publishing Research Quarterly》2004,19(4):31-36
Summary Now more than ever good translations are proving to be vitally important to fulfilling Shoichi Noma’s mission of advancing
good relationships between cultures. With the emergence of dozens of world-class Japanese writers, the translators of Japanese
literature have acquired a new significance and a new importance. No longer are they required to simply handle the language
like an exotic and precious commodity but they must prepare a writer’s work to compete with literature from around the globe
and let others understand the issues at their very heart of the Japanese people.
Indeed, the selection committee’s choice of Murakami’s Wind-Up Bird Chronicle is an apt example of how literature at its very best can span cultural divides to promote mutual understanding of very complicated
and important world issues. Written for Japanese to help them understand the devastation of war, it is a rare and timely book
that confronts Japan’s experience head on. Ironically, Noma’s idea that books are silent ambassadors, an idea born from agony
and defeat, so many years later is helping the world come together towards a unified vision of peace and understanding.
“Literature has been described as a means of exchange between the hearts of people. Literary translation, then, is the building
of bridges so that all peoples of the world can interact in this way.”
Ye Weiqu, Chinese Institute of Social Studies. 相似文献
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Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome. 相似文献