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Gary Burnett Paul T. Jaeger Kim M. Thompson 《Library & information science research》2008,30(1):56-66
Information access is central to library and information science, yet explorations of its conceptual nature have been limited. Given the importance of information access to the discipline, there is a need for research to create a better understanding of the concept and its many roles in all activities and behaviors related to information. Drawing on the theoretical work of Elfreda Chatman, this article proposes that the study of information access can be facilitated through the recognition and examination of the physical, intellectual, and social aspects of information access. These types of access are examined through three case studies in terms of different information behaviors and contexts, with a particular focus on the importance of social access. The article also discusses the future roles that considerations of social access can play in research and theory. 相似文献
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In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve
this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments.
Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it
is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach
is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study,
we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations
and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form
query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation
shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on
TREC retrieval. 相似文献
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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