Name
Abstract | ||
|---|---|---|
Document retrieval systems based on probabilistic or fuzzy logic considerations may order documents for retrieval. Users then examine the ordered documents until deciding to stop, based on the estimate that the highest ranked unretrieved document will be most economically not retrieved. We propose an expected precision measure useful in estimating the performance expected if yet unretrieved documents were to be retrieved, providing information that may result in more economical stopping decisions. An expected precision graph, comparing expected precision versus document rank, may graphically display the relative expected precision of retrieved and unretrieved documents and may be used as a stopping aid for online searching of text data bases. The effectiveness of relevance feedback may be examined as a search progresses. Expected precision values may also be used as a cutoff for systems consistent with probabilistic models operating in batch modes. Techniques are given for computing the best expected precision obtainable and the expected precision of subject neutral documents. |
Year | DOI | Venue |
|---|---|---|
1987 | 10.1016/0306-4573(87)90057-4 | Inf. Process. Manage. |
Keywords | Field | DocType |
document retrieval,relevance information retrieval,system performance,information retrieval,graphs,documentation,feedback,performance | Data mining,Relevance feedback,Information retrieval,Ranking,Computer science,Fuzzy logic,Automation,Relevance (information retrieval),Document retrieval,Probabilistic logic,Documentation | Journal |
Volume | Issue | ISSN |
23 | 6 | 0306-4573 |
Citations | PageRank | References |
3 | 0.60 | 21 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert M. Losee | 1 | 276 | 36.01 |