Topic Detection and Tracking: Event-based Information OrganizationJames Allan Springer Science & Business Media, 28.02.2002 - 266 Seiten 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going on for about five years, and publications related to it are scattered all over the place as technical reports, unpublished manuscripts, or in numerous conference proceedings.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- |
Inhalt
Introduction to Topic Detection and Tracking | 3 |
2 TDT tasks | 5 |
3 History of TDT | 9 |
4 TDT 1999 and TDT 2000 | 12 |
5 The Future of TDT | 15 |
Topic Detection and Tracking Evaluation Overview | 19 |
Stories Events and Topics | 20 |
3 TDT Corpora | 21 |
5 Summary | 134 |
Segmentation and Detection at IBM Hybrid Statistical Models and Twotiered Clustering | 137 |
2 Topic Detection | 144 |
3 Acknowledgements | 149 |
A ClusterBased Approach to Broadcast News | 151 |
2 Segmentation | 154 |
3 Detection | 156 |
4 Tracking | 165 |
4 Evaluation Methodology | 22 |
5 Task Definitions | 27 |
Corpora for Topic Detection and Tracking | 35 |
2 Overview of TDT Corpus Development | 37 |
3 Collection of Raw Data | 38 |
4 Transcription | 40 |
5 Story Segmentation | 41 |
6 Topic Definition | 44 |
7 Topic Annotation | 47 |
8 Corpus Formats | 56 |
9 Some Properties of the Corpus | 63 |
10 Conclusion | 66 |
Probabilistic Approaches to Topic Detection and Tracking | 69 |
2 Core TDT Technologies | 70 |
3 Corpus Processing | 77 |
5 Detection | 79 |
6 Crosslingual TDT | 82 |
7 Conclusions and Future Work | 83 |
Multistrategy Learning for Topic Detection and Tracking A joint report of CMU approaches to multilingual TDT | 87 |
2 Segmentation | 89 |
3 Topic and Event Tracking | 90 |
4 Topic Detection | 98 |
5 First Story Detection | 101 |
6 Story Link Detection | 103 |
7 Multilingual TDT | 109 |
8 Concluding Remarks | 113 |
Statistical Models of Topical Content | 117 |
2 Models of Story Generation | 119 |
3 Tracking Systems | 122 |
4 Detection System | 130 |
5 Acknowledgements | 175 |
Signal Boosting for Translingual Topic Tracking Document Expansion and nbest Translation | 177 |
1 Introduction | 178 |
2 The SignaltoNoise Perspective | 179 |
3 Topic Tracking System Architecture | 180 |
4 Contrastive Conditions | 186 |
5 Conclusions and Future Work | 193 |
Explorations Within Topic Tracking and Detection | 199 |
2 Basic System | 200 |
3 Tracking | 205 |
4 Cluster Detection | 207 |
5 First Story Detection | 210 |
7 Bounds on Effectiveness | 218 |
8 Automatic Timeline Generation | 221 |
9 Conclusions | 224 |
Towards a Universal Dictionary for MultiLanguage Information Retrieval Applications | 227 |
2 Our TDT tracking algorithm | 231 |
3 The Universal Dictionary experiment | 238 |
4 Conclusions and Directions for Future Work | 241 |
An NLP IR Approach to Topic Detection | 245 |
2 General System Framework | 247 |
3 Representation of News Stories and Topics | 248 |
Method | 250 |
5 Multilingual Topic Detection | 252 |
6 Development Experiments | 258 |
7 Evaluation | 261 |
8 Discussion | 263 |
9 Concluding Remarks and Future Works | 264 |
Andere Ausgaben - Alle anzeigen
Topic Detection and Tracking: Event-based Information Organization James Allan Eingeschränkte Leseprobe - 2012 |
Topic Detection and Tracking: Event-based Information Organization James Allan Keine Leseprobe verfügbar - 2012 |
Häufige Begriffe und Wortgruppen
90 False Alarms algorithm Allan annotation approach audio automatic Beta-Binomial Cdet centroid Chinese cluster detection collection combined computed cosine cross-language cross-language information retrieval curve DARPA deferral Detection and Tracking detection system detection task distribution document expansion English and Mandarin event False Alarms probability Information Retrieval Inquery language modeling Linguistic Data Consortium Link Detection measure microcluster Miss probability monolingual multilingual named entities NIST nouns off-topic on-topic stories P(Fa P(Miss parameters query query expansion Random Performance relevant score normalization selected SGML SIGIR similarity sources statistics story boundaries Story Detection Story Link story pairs Story Segmentation strategy Systran Table target TDT evaluation TDT pilot TDT tasks TDT-2 corpus TDT2 techniques term list test story Text Retrieval Conference tf.idf topic cluster Topic Detection topic tracking Topic Weighted Tracker tracking system tracking task training data training stories transcripts translingual TREC unigram vector words wsum
Beliebte Passagen
Seite iv - Kang Wu; Mohan S. Kankanhalli;Joo-Hwee Lim;Dezhong Hong; ISBN: 0-7923-7944-6 MINING THE WORLD WIDE WEB : An Information Search Approach, by George Chang, Marcus J. Healey, James AM McHugh, Jason TL Wang; ISBN: 0-7923-7349-9 INTEGRATED REGION-BASED IMAGE RETRIEVAL, by James Z. Wang; ISBN: 0-7923-7350-2 TOPIC DETECTION AND TRACKING: Event-based Information Organization, Language Modeling for Information Retrieval Edited by W.
Seite iv - Lalmas, and Cornells Joost van Rijsbergen; ISBN: 0-7923-8302-8 DOCUMENT COMPUTING: Technologies for Managing Electronic Document Collections, by Ross Wilkinson, Timothy Arnold-Moore, Michael Fuller, Ron Sacks-Davis, James Thorn, and Justin Zobel; ISBN: 0-7923-8357-5 AUTOMATIC INDEXING AND ABSTRACTING OF DOCUMENT TEXTS, by MarieFrancine Moens; ISBN 0-7923-7793-1 ADVANCES IN INFORMATIONAL RETRIEVAL: Recent Research from the Center for Intelligent...
Verweise auf dieses Buch
Computational Linguistics and Intelligent Text Processing: 5th International ... Alexander Gelbukh Keine Leseprobe verfügbar - 2004 |