The pragmatics of information retrieval experimentation

The novice information scientist, though he or she may have thoroughly studied the design and results of previous information retrieval tests and clearly described the purpose of his/her own test, may still, when faced with its implementation, have great difficulty in proceeding. Early information retrieval experiments were of necessity ad hoc, and it is only in recent years that a body of practice, based on the experiences of Cleverdon and later investigators, has made possible a few recommendations on the pragmatics of conducting information retrieval experiments. The following remarks, though based to some extent on a study of the major tests, including those described in later chapters of this book, are heavily dependent on the author's own trials, tribulations, and mistakes. If there is one lesson to be learned from experience, it is that the theoretically optimum design can never be achieved, and the art of information retrieval experimentation is to make the compromises that will least detract from the usefulness of the results. In determining experimental procedures, three aspects must be kept in mind:

[1]  F. Yates,et al.  Statistical Tables for Biological, Agricultural and Medical Research. , 1939 .

[2]  J. Wishart Statistical tables , 2018, Global Education Monitoring Report.

[3]  Cyril W. Cleverdon,et al.  Factors determining the performance of indexing systems , 1966 .

[4]  Michael E. Lesk,et al.  Relevance assessments and retrieval system evaluation , 1968, Inf. Storage Retr..

[5]  J. Walsh Elements of Nonparametric Statistics , 1968 .

[6]  T. Hayton The Advanced Theory of Statistics, Vol. 3 , 1968 .

[7]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[8]  N. Lemon Attitudes and their measurement. , 1973 .

[9]  Howard B. Lee,et al.  Foundations of Behavioral Research , 1973 .

[10]  L. Ott,et al.  Statistics: A Tool for the Social Sciences. , 1975 .

[11]  Tefko Saracevic,et al.  RELEVANCE: A review of and a framework for the thinking on the notion in information science , 1997, J. Am. Soc. Inf. Sci..

[12]  Clement T. Yu,et al.  Automatic indexing using term discrimination and term precision measurements , 1976, Information Processing & Management.

[13]  K. Sparck Jones,et al.  INFORMATION RETRIEVAL TEST COLLECTIONS , 1976 .

[14]  Michael D. Cooper Input-output relationships in on-line bibliographic searching , 1977, J. Am. Soc. Inf. Sci..

[15]  Alan R. Benenfeld,et al.  Catalog information and text as indicators of relevance , 1978, J. Am. Soc. Inf. Sci..

[16]  John C. Huber Introduction to the use of computer packages for statistical analyses , 1978 .

[17]  Jean Tague-Sutcliffe,et al.  Estimation and reliability of retrieval effectiveness measures , 1978, Inf. Process. Manag..

[18]  Karen Sparck Jones,et al.  Statistical bases of relevance assessment for the ideal information retrieval test collection , 1979 .