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Nelson, Michael J.; Tague, Jean M. – Journal of the American Society for Information Science, 1985
Proposes split model for index term distribution in document set that uses rank function for high frequency terms and size function for low frequency terms; the point of transition is determined either empirically or by rule. Distributions to describe index term exhaustivity and term co-occurrence are considered briefly. (36 references) (EJS)
Descriptors: Databases, Indexing, Information Retrieval, Models

Mahapatra, M.; Biswas, S. C. – International Library Review, 1984
Describes research which measured the efficiency of role operators through frequency of appearances in PRECIS input strings for 200 abstracts related to taxation, genetic psychology, and Shakespearian drama. Frequencies of appearance of major categories of role operators, role operators in different subjects, individual main line operators, and…
Descriptors: Comparative Analysis, Indexing, Information Retrieval, Relevance (Information Retrieval)

Hood, William; Wilson, Concepcion S. – Information Processing and Management, 1994
Summarizes the findings of a recent study on the indexing practices used in the Library and Information Science Abstracts (LISA) database. Descriptors, or indexing terms, from the thesaurus are analyzed; searching implications are discussed; and the relationship between the classification code and the descriptors is examined. (Contains 21…
Descriptors: Bibliographic Databases, Classification, Indexing, Library Science

Trivison, Donna – Information Processing and Management, 1987
Describes study that compared terminology indexing and citation indexing to determine the similarity of documents retrieved. Information science articles from 1971 to 1983 in three journals were studied for term co-occurrences. The distinction between relevant and pertinent documents is discussed, and further research topics are suggested. (LRW)
Descriptors: Bibliographic Coupling, Citation Indexes, Comparative Analysis, Indexing

Smith, F. J.; Devine, K. – Information Processing and Management, 1985
Zipfian laws for frequency distributions of word pairs and longer phrases are derived from text sample analysis. From crossing of Zipfian curves, it is deduced that number of multi-word phrases that occur frequently in text is surprisingly small, of same order of magnitude as number of individual word-types. (8 references) (EJS)
Descriptors: Algorithms, Graphs, Indexing, Information Retrieval