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A Statistical Approach to Mechanized Encoding and Searching of Literary Information

Luhn, H. P.·1957·IBM Journal of Research and Development

Abstract

Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of “notions” and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.

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Metadata

Authors
Luhn, H. P.
Venue
IBM Journal of Research and Development
Publisher
IBM
Published
1957-10
Volume
1
Issue
4
Pages
309-317
ISSN
0018-8646 (ISSN-L)
OpenAlex
W1985697096
Cited by
1,093
References
0
FWCI
35.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Statistical Approach to Mechanized Encoding and Searching of Literary Information. https://app.cochat.ai/papers/10.1147/rd.14.0309
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Luhn, H. P. (1957). A Statistical Approach to Mechanized Encoding and Searching of Literary Information. IBM Journal of Research and Development. https://doi.org/10.1147/rd.14.0309

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.