Library school education for medical librarianship.

This paper reviews the current situation in library school education for medical librarianship in the United States and Canada based on information from a questionnaire sent to teachers of courses in medical librarianship in accredited library schools. Since 1939, when the first course devoted entirely to medical librarianship was offered at Columbia University, courses have been introduced into the curricula of at least forty-seven of the ALA-accredited library schools. In 1978 there were seventy courses available through forty-seven library schools. Possibilities for specialization in medical librarianship are examined. Course content is reviewed. Implications of the MLA certification examination for library school courses are explored.     

Editorial

This issue of the Journal contains three interesting articlesabout central aspects of public opinion research, as well asan equal number of research notes that reflect the range oftopics that interest all public opinion researchers. Toshio Takeshita takes a turn at reconceptualizing agenda-settingstudies, using a taxonomy     

Unique unto itself: The records of the Federal Bureau of Investigation 1908 to 1945

This paper addresses several misunderstandings about the nature of the FBI's Central Records System. Although depicted in popular culture and much of FBI historiography as a tool for the Cold War suppression of American radicals, the FBI's records system grew out of Department of Justice systems already in place before J. Edgar Hoover became Director in 1924. The system grew in size and complexity according to the demands placed on it by the changing priorities of the FBI's criminal and intelligence missions, guided by a “progressive” drive for efficiency through centralization.     

Analysis of Statistical Question Classification for Fact-Based Questions

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.