The Archival Bond

This paper presents the concept of archival bond as formulated by archival science and used in a research project carried out at the University of British Columbia, entitled The Preservation of Electronic Records. Being one of the essential components of the record, the concept of archival bond is discussed in the context of the traditional diplomatic and archival definitions of records, and its function in demonstrating the reliability and authenticity of records is shown. The most serious challenge with which we are confronted is to make explicit and preserve intact over the long term the archival bond between electronic and non electronic records belonging in the same aggregations.     

The Concept of Record in Interactive, Experiential and Dynamic Environments: the View of InterPARES*

This article presents the concept of electronic record as articulated by the first phase of the InterPARES (International research on Permanent Authentic Records in Electronic Systems) Project (1999–2001) and discusses it in light of the findings of the second phase of the Project (2002–2006). While InterPARES 1 focused on records produced and/or maintained in databases and document management systems, InterPARES 2 examined records produced and/or maintained in interactive, experiential and dynamic environments. The authors describe the characteristics of these environments and of the entities found in them in the course of case studies conducted on systems used for carrying out artistic, scientific and e-government activities, and propose the new concept of record that InterPARES 2 is eleborating, which expands on that formulated by InterPARES 1.The authors would like to thank InterPARES 2 research assistant Randy Preston for his careful editing and constructive criticisms. Some of his suggestions have been incorporated in the text of this article.     

Books are silent ambassadors: The Noma award for the translation of Japanese literature

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.     

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.