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Another look at automatic text-retrieval systems
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Communications of the ACM archive
Volume 29 ,  Issue 7  (July 1986) table of contents
Pages: 648 - 656  
Year of Publication: 1986
ISSN:0001-0782
Author
Gerard Salton  Cornell Univ., Ithaca, NY
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 15,   Downloads (12 Months): 131,   Citation Count: 77
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ABSTRACT

Evidence from available studies comparing manual and automatic text-retrieval systems does not support the conclusion that intellectual content analysis produces better results than comparable automatic systems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Cleverdon. C.W. A computer evaluation of searching by controlled language and natural language in an experimental NASA data base. Rep. ESA l/432, European Space Agency, Frascati. Italy, July 1977. A description of a large-scale test of the NASA search system using various manual and automatic text-analysis methods.
 
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Cleverdon. C.W., and Keen, E.M. Aslib-Cranfield Research Project. Vol. 2. Test Results. Cranfield Institute of Technology, Cranfield. England, 1966. The report on the most thorough evaluation of automatic versus manual text-analysis methods ever carried out, using a collection of 1400 aeronautics documents.
 
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Croft, W.B., and Harper, O.J. Using probabilistic models of document retrieval without relevance information. 1. Dot. 35, 4 (Dec. 1979). 285-295. Describes a method for using probabilistic considerations of term relevance for an initial collection search before any relevance information is available.
 
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IBM World Trade Corporation. Storage and Znformafion Refrieval Sysfern (STAIRS}-General Iilformafion Manual. 2nd ed. IBM Germany. Stuttgart, Germany. Apr. 1972. Contains an early description of the IBM/STAIRS system.
 
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Lancaster, F.W. Evaluation of fhe Medlars Demand Search Service. National Library of Medicine, Bethesda, Md., Jan. 1968. An impressive description of the in-house test of the Medlars search system carried out at the National Library of Medicine.
 
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Lancaster, F.W. information Retrieval Systems: Clraracferistics, Testing, and EualuaGon. 2nd ed. Wiley, New York, 1979. A well-known textbook in information retrieval with an emphasis on system testing and evaluation.
 
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Lovins, J.B. Development of a stemming algorithm. Mech. Transl. Comput. Linguist. II. 1-2 (Mar. and June 1968), 11-31. A detailed description of an automatic word-stemming algorithm.
 
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Robertson, SE.. and Sparck Jones, K. Relevance weighting of search terms. 1. ASIS 27, 3 (May-June 1976), 129-146. Describes one of the main probabilistic information-retrieval models.
 
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Salton, G. Automatic text analysis. Science 168, 3929 (Apr. 1970), 335-343. A survey of automatic text retrieval as of 1970.
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Salton, G.. Yang, C.S., and Yu, CT. A theory of term importance in automatic text analysis. I. ASK 26, 1 (Jan.-Feb. 1975), 33-44. Contains a description of term-discrimination theory and some retrieval results based on discrimination value weighting.
 
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Sparck Jones, K. A statistical interpretation of term specificity and its application in retrieval. J Dot. 28, 1 (Mar. 1972), 11-21. Relates the usefulness of index terms to certain statistical term occurrence parameters.
 
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Swanson. D.R. Searching natural language text by computer. Science 132, 3434 (Oct. 1960), 1099-1104. A pioneering small-scale test comparing an automatic text-search system with a conventional retrieval system based on manual indexing: probably the earliest result showing the superiority of automatic text searching.
 
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CITED BY  77


REVIEW

"Robert G Crawford : Reviewer"

In a document retrieval system, a file of natural-language documents is searched and certain stored items are retrieved in response to queries submitted by users. A research question concerns the effectiveness of fully automated document retriev  more...