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This paper describes the procedures used at the Library of the College of Physicians of Philadelphia to divide its ninety-year-old dictionary card catalog. The division was necessitated by overcrowding, obsolete subject headings, and lack of a complete authority list which resulted in like materials being scattered throughout the catalog under several headings. Two catalogs were created: the historical-biographical catalog, representing all works published before 1950 and all works of historical or biographical nature; and the current catalog, containing all works published from 1950 on, excepting historical or biographical materials. The 1950- catalog was further divided into name and subject catalogs, and the subject section was revised according to MeSH. The project was completed in about two years. As a result, searching time has been much reduced, and the library is able to take advantage of the annual revisions of MeSH to update the subject catalog. 相似文献
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Sandra L De Groote Marceline Doranski 《Journal of the Medical Library Association》2004,92(3):341-348
OBJECTIVES: The purpose of this study is to determine how personal digital assistants (PDAs) are used on an academic health sciences campus to define the level of training and support the library can provide to the students and faculty. METHOD: A Web-based questionnaire was developed. A total of 1,538 health sciences faculty and residents were sent an email message requesting participation. Data from the returned surveys were analyzed with SPSS. RESULTS: Sixty-one percent of survey respondents used PDAs. The address book, date book, and calculator were the most common uses reported for PDAs. Residents also reported a high use of drug databases on their PDAs. Most survey respondents indicated they would like to learn more about clinical resources for PDAs. CONCLUSIONS: Many opportunities exist for librarians to provide training and support for PDAs, in addition to evaluation and promotion of clinical software for PDAs. 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