Combining Automatic and Manual Index Representations in Probabilistic Retrieval

Combining Automatic and Manual Index Representations in Probabilistic Retrieval (1995)

by T. B. Rajashekar , W. Bruce Croft

Venue:	Journal of the American Society for Information Science
Citations:	32 - 5 self

Datum	Value	Source
TITLE	Combining Automatic and Manual Index Representations in Probabilistic Retrieval	SVM HeaderParse 0.1
AUTHOR NAME	T. B. Rajashekar	SVM HeaderParse 0.1
AUTHOR ADDR	Bangalore 560 012, India	SVM HeaderParse 0.1
AUTHOR NAME	W. Bruce Croft	SVM HeaderParse 0.1
AUTHOR ADDR	Amherst, MA 01003	SVM HeaderParse 0.1
ABSTRACT	Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representations, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidential reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. In this paper, we use a system based on this model to study the retrieval effectiveness benefits of combining the types of document and query information that are found in typical commercial databases and information services. The results indicate that substantial real benefits are possible. 1	SVM HeaderParse 0.1
CITATIONS	29 found	ParsCit 1.0