Document classification using multiword features
Proceedings of the seventh international conference on Information and knowledge management
Evaluating database selection techniques: a testbed and experiment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Phrase recognition and expansion for short, precision-biased queries based on a query log
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Comparing the performance of database selection algorithms
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mobile delivery of news using hierarchical query-biased summaries
Proceedings of the 2002 ACM symposium on Applied computing
Information Retrieval from Documents: A Survey
Information Retrieval
Using the Co-occurrence of Words for Retrieval Weighting
Information Retrieval
Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks
Information Retrieval
Enhancing Text Retrieval by Using Advanced Stylistic Techniques
Journal of Intelligent and Robotic Systems
Information Processing and Management: an International Journal
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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There have been four Text REtrieval Conferences (TRECs); TREC-1 in November 1992, TREC-2 in August 1993, TREC-3 in November 1994 and TREC-4 in November 1995. The number of participating systems has grown from 25 in TREC-1 to 36 in TREC-4, including most of the major text retrieval software companies and most of the universities doing research in text retrieval (see table for some of the participants). The diversity of the participating groups has ensured that TREC represents many different approaches to text retrieval, while the emphasis on individual experiments evaluated in a common setting has proven to be a major strength of TREC.