ABSTRACT
An automatic document retrieval system, programmed for the IBM 7094, is described. The system is designed to process English texts and search requests, and uses statistical, syntactic and semantic procedures for the analysis of information and the identification of relevant items.
The operations are planned around a central supervisor, which in turn calls on the various subroutines, as desired. This organization makes it possible to alter both the processing sequences and the matching criteria between stored information and search requests, thus producing a variable amount of information in response to a given search request. The system therefore lends itself to interaction with the user by enabling the latter continuously to change his search requirements. Furthermore, the various analytical procedures may be evaluated by comparing the retrieval results obtained under different processing conditions.
- 1.American Library Association, "The Library and Information Networks in the Future," Report to Rome Air Development Center, April 1963, AD 401 347.Google Scholar
- 2.G. W. King et al, "Automation and the Library of Congress," Library of Congress, Washington, 1963.Google Scholar
- 3.Information Storage and Retrieval, Report No. ISR-7 to the National Science Foundation, Harvard Computation Laboratory, June 1964.Google Scholar
- 4.E. H. Sussenguth, Jr., "Use of Tree Structures for Processing Files," Communications of the ACM, Vol. 6, No. 5, May 1963. Google ScholarDigital Library
- 5.G. Salton, "Some Experiments in the Generation of Word and Document Associations," Proceedings of the Fall Joint Computer Conference, Philadelphia, December 1962.Google Scholar
- 6.J. C. Gardin and F. Levy, "Le Syntol - Syntagmatic Organization Language," Proceedings of the IFIP Congress-62, Munich, 1962.Google Scholar
- 7.G. Salton, "Some Hierarchical Models for Document Retrieval," American Documentation, Vol. 14, No. 3, July 1963.Google Scholar
- 8.G. Salton, "Associative Document Retrieval Techniques Using Bibliographic Information," Journal of the ACM, Vol. 10, No. 4, October 1963. Google ScholarDigital Library
- 9.H. P. Luhn, "The Automatic Creation of Literature Abstracts," IBM Journal of Research and Development, Vol. 2, No. 2, April 1958.Google ScholarDigital Library
- 10.S. Kuno and A. G. Oettinger, "Multiple-path Syntactic Analyzer," Proceedings of the IFIP Congress-62, Munich, 1962.Google Scholar
- 11.G. Salton, "Manipulation of Trees in Information Retrieval," Communications of the ACM, Vol. 5, No. 2, February 1962. Google ScholarDigital Library
- 12.E. H. Sussenguth, Jr., "Automatic Structure Matching Procedures in Information Processing," Report ISR-6 to the National Science Foundation, Harvard Computation Laboratory, April 1964.Google Scholar
- 13.G. Salton and E. H. Sussenguth, Jr., "Some Flexible Information Retrieval Systems Using Structure Matching Procedures," AFIPS Spring Joint Computer Conference, Washington, April 1964.Google Scholar
- 14.L. B. Doyle, "Indexing and Abstracting by Association," American Documentation, Vol. 13, No. 4, October 1962.Google Scholar
- 15.H. E. Stiles, "The Association Factor in Information Retrieval," Journal of the ACM, Vol. 8, No. 2, April 1961. Google ScholarDigital Library
- 16.C. W. Cleverdon, "The Testing of Index Language Devices," ASLIB Proceedings, Vol. 15, No. 4, April 1963.Google Scholar
- 17.V. E. Giuliano et al, "Centralization and Documentation," Final Report to the National Science Foundation, No. C-64469, Arthur D. Little, Inc., July 1963.Google Scholar
Index Terms
A document retrieval system for man-machine interaction
Recommendations
Cross-language spoken document retrieval using HMM-based retrieval model with multi-scale fusion
Cross-language spoken document retrieval (CL-SDR) is the technology that facilitates automatic retrieval of relevant information from a collection of spoken documents in a language that is different from that used in the queries. Information sources ...
Document expansion for image retrieval
RIAO '10: Adaptivity, Personalization and Fusion of Heterogeneous InformationSuccessful information retrieval requires effective matching between the user's search request and the contents of relevant documents. Often the request entered by a user may not use the same topic relevant terms as the authors' of these documents. One ...
Non-relevance Feedback for Document Retrieval
KAM '09: Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 02We need to find documents that relate to human interesting from a large data set of documents. The relevance feedback method needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are ...
Comments