Topic Detection and Tracking: Event-based Information Organization - the Information Retrieval Series

Marc Notes: Includes bibliographical references and index. Table of Contents: Preface. 1. Introduction to Topic Detection and Tracking; J. Allan. 2. Topic Detection and Tracking Evaluation Overview; J. G. Fiscus, G. R. Doddington. 3. Corpora for Topic Detection and Tracking; C. Cieri, et al. 4. Probabilistic Approaches to Topic Detection and Tracking; T. Leek, et al. 5. Multi-strategy Learning for TDT; Yiming Yang, et al. 6. Statistical Models of Topical Content; J. P. Yamron, et al. 7. Segmentation and Detection at IBM; S. Dharanipragada, et al. 8. A Cluster-Based Approach to Broadcast News; D. Eichmann, P. Srinivasan. 9. Signal Boosting for Translingual Topic Tracking; G.-A. Levow, D. W. Oard. 10. Explorations Within Topic Tracking and Detection; J. Allan, et al. 11. Towards a Universal Dictionary' for Multi-Language IR Applications; J. Michael Schultz, M. Y. Liberman. 12. An NLP & IR Approach to Topic Detection; Hsin-Hsi Chen, Lun-Wei Ku. Index. Publisher Marketing: The purposeofthis book is to providea recordofthe stateofthe art in Topic Detection and Tracking (TDT) in a single place. Research in TDT has been going on for about five years, and publications related to it are scattered all over the place as technical reports, unpublished manuscripts, or in numerous conference proceedings. The third and fourth in a series of on-going TDT evaluations marked a turning point in the research. As such. it provides an excellent time to pause. review the state of the art. gather lessons learned, and describe the open challenges. This book is a collection oftechnical papers. As such, its primary audience is researchers interested in the the current state of TDT research, researchers who hope to leverage that work sothat theirown efforts can avoid pointlessdu plication and false starts. It might also pointthem in the direction ofinteresting unsolved problems within the area. The book is also of interest to practition ers in fields that are related to TDT--e.g., Information Retrieval. Automatic Speech Recognition. Machine Learning, Information Extraction, and so on. In thosecases, TDTmay provide arich application domain for theirown research, or it might address similarenough problems that some lessons learned can be tweaked slightly to answer-perhaps partiallY-"

Contributor Bio:  Allan, James James Allan is a professor in the department of Eastern art, Ashmolean Museum, Oxford. Sheila Canby is a curator in the department of Oriental antiquities at the British Museum. Jon Thompson is a textile specialist.