Classification Space: A Multivariate Procedure for Automatic Document Indexing and Retrieval Public Deposited
  • A conceptual approach to linguistic data processing problems is sketched and empirical illustrations are presented of the major software components indexing, storage, and retrieval-of a document processing system which offers, in principle, the advantages of complete automation, unlimited cross-indexing, effective sequential retrieval, sub-documentary indexing reflecting heterogeneity of subject matter within a document, and a procedure for automatically identifying retrieval requests which would be inadequately handled by the system. The indexing schema, designated as a "Classification Space" consists of a Euclidean model for mapping subject matter similarity within a given subject matter domain. A schema of this kind is empirically derived for certain fields of Engineering and Chemistry. A set of five related empirical studies provide convincing evidence that when appropriate experimental procedures are followed a very stable C-Space for a given content domain can be constructed on a surprisingly small data base. Other empirical studies demonstrate specific computational procedures for effective automatic indexing of documents in a C-Space, using a relatively small system vocabulary. One study demonstrates that a C-Space maps subject matter relevance as well as subject matter similarity, and thereby promotes effective sequential retrieval; this result is also shown under conditions of automatic indexing. Negative results are found in an attempt to use the structural linguistic distinction of subject and object as a means of improving techniques for automatic indexing.
Date Issued
  • 1966-10-01
Academic Affiliation
Last Modified
  • 2019-12-09
  • scap_ossorioLRI_1_classificationSpace
Resource Type
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