论文信息 - Threshold setting in adaptive filtering

Threshold setting in adaptive filtering

A major problem in using current best‐match methods in a filtering task is that of setting appropriate thresholds, which are required in order to force a binary decision on notifying a user of a document. We discuss methods for setting such thresholds and adapting them as a result of feedback information on the performance of the profile. These methods fit within the probabilistic approach to retrieval, and are applied to a probabilistic system. Some experiments, within the framework of the TREC‐7 adaptive filtering track, are described.

Stephen E. Robertson | Stephen Walker | S. Robertson | S. Walker

[1] James P. Callan. Learning while filtering documents , 1998, SIGIR '98.

[2] Stephen E. Robertson,et al. Okapi at TREC-7: Automatic Ad Hoc, Filtering, VLC and Interactive , 1998, TREC.

[3] Stephen E. Robertson,et al. Okapi at TREC-6 Automatic ad hoc, VLC, routing, filtering and QSDR , 1997, TREC.

[4] Kui-Lam Kwok,et al. TREC-7 Ad-Hoc, High Precision and Filtering Experiments using PIRCS , 1998, TREC.

[5] K. Sparck Jones,et al. A Probabilistic Model of Information Retrieval : Development and Status , 1998 .

[6] Abraham Bookstein,et al. Information retrieval: A sequential learning process , 1983, J. Am. Soc. Inf. Sci..

[7] Ellen M. Voorhees,et al. The seventh text REtrieval conference (TREC-7) , 1999 .

[8] Glenn Carroll,et al. Learn-ing probaballstic dependency grammars from labelled text , 1992 .

[9] Stephen E. Robertson,et al. Relevance weighting of search terms , 1976, J. Am. Soc. Inf. Sci..

[10] Stephen E. Robertson,et al. Overview of the Okapi projects , 1997, J. Documentation.

[11] David A. Hull. The TREC-7 Filtering Track: Description and Analysis , 1998, Text Retrieval Conference.

[12] Fredric C. Gey,et al. Experiments in the Probabilistic Retrieval of Full Text Documents , 1994, TREC.