Probabilistic approaches to topic detection and tracking

Authors:
Tim Leek;Richard Schwartz;Srinivasa Sista
Affiliations:
BBN Technologies, Speech and Language Processing, Cambridge, MA;BBN Technologies, Speech and Language Processing, Cambridge, MA;BBN Technologies, Speech and Language Processing, Cambridge, MA
Venue:
Topic detection and tracking
Year:
2002

Citing 1
Cited 11

A hidden Markov model information retrieval system

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval

Language-specific models in multilingual topic tracking

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Usefulness of temporal information automatically extracted from news articles for topic tracking

ACM Transactions on Asian Language Information Processing (TALIP)
Dictionary-based techniques for cross-language information retrieval

Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
NLP and IR approaches to monolingual and multilingual link detection

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Comparative study of monolingual and multilingual search models for use with asian languages

ACM Transactions on Asian Language Information Processing (TALIP)
Relevance models for topic detection and tracking

HLT '02 Proceedings of the second international conference on Human Language Technology Research
Web video topic discovery and tracking via bipartite graph reinforcement model

Proceedings of the 17th international conference on World Wide Web
New event detection and topic tracking in Turkish

Journal of the American Society for Information Science and Technology
Recommendation in Internet forums and blogs

ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
User comments for news recommendation in forum-based social media

Information Sciences: an International Journal
Double-pass clustering technique for multilingual document collections

Journal of Information Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

BBN's systems for TDT use probabilistic models for higher accuracy and easy training. They generate measures that are normalized across topics, so that only one threshold is necessary to make decisions. These systems make little or no use of deep linguistic knowledge, and therefore are easy to modify for new languages and domains. At the same time their performance has consistently been in the top tier.