This paper describes and analyzes the results of the Third Message Understanding Conference (MUC-3). It reviews the purpose, history, and methodology of the conference, summarizes the participating systems, discusses issues of measuring system effectiveness, describes the linguistic phenomena tests, and provides a critical look at the evaluation in terms of the lessons learned. One of the common problems with evaluations is that the statistical significance of the results is unknown. In the discussion of system performance, the statistical significance of the evaluation results is reported and the use of approximate randomization to calculate the statistical significance of the results of MUC-3 is described

This report develops a conceptual framework for text filtering practice and research, and reviews present practice in the field. Text filtering is an information seeking process in which documents are selected from a dynamic text stream to satisfy a relatively stable and specific information need. A model of the information seeking process is introduced and specialized to define information filtering. The historical development of text filtering is then reviewed and case studies of recent work are used to highlight important design characteristics of modern text filtering systems. Specific techniques drawn from information retrieval, user modeling, machine learning and other related fields are described, and the report concludes with observations on the present state of the art and implications for future research on text filtering.

This paper develops a conceptual framework for text filtering practice and research, and reviews present practice in the field. Text filtering is an information seeking process in which documents are selected from a dynamic text stream to satisfy a relatively stable and specific information need. A model of the information seeking process is introduced and specialized to define text filtering. The historical development of text filtering is then reviewed and case studies of recent work are used to highlight important design characteristics of modern text filtering systems. User modeling techniques drawn from information retrieval, recommender systems, machine learning and other fields are described. The paper concludes with observations on the present state of the art and implications for future research on text filtering.