The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automatic Indexing: An Experimental Inquiry
Open accessAbstract
This inquiry examines a technique for automatically classifying (indexing) documents according to their subject content.The task, in essence, is to have a computing machine read a document and on the basis of the occurrence of selected clue words decide to which of many subject categories the document in question belongs.This paper describes the design, execution and evaluation of a modest experimental study aimed at testing empirically one statistical technique for automatic indexing.
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“The 1950s also witnessed the first applications of the Naïve Bayes (NB) classifier in the fields of pattern recognition [97] and information retrieval [297].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [297]
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Citations
Cites
On Relevance, Probabilistic Indexing and Information Retrieval
Maron, M. E., & Kuhns, J. L. (1960) · Journal of the ACM · doi:10.1145/321033.321035 · Cited by 908
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Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,927
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140
“Its use in the form of Equation 11 was promoted by Robertson and Sparck Jones in a paper [41] that did much to clarify and unify a number of related and partially ad hoc applications of naive Bayes dating back to Maron [37].”
BackgroundMethodA sequential algorithm for training text classifiersPreprint
Lewis, D., & Gale, W. A. (1994) · arXiv (Cornell University) · doi:10.5555/188490.188495 · Cited by 1,838
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600
“Early work on a probabilistic classifier and its text classification performance was reported by Maron [24].”
BackgroundThe text mining handbook: advanced approaches in analyzing unstructured data
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5684 · Cited by 1,600
Metadata
- Authors
- Maron, M. E.
- Affiliations
- RAND Corporation (US)
- Corresponding author
- Maron, M. E.
- Venue
- Journal of the ACM
- Published
- 1961-07
- Volume
- 8
- Issue
- 3
- Pages
- 404-417
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W2009190245
- Cited by
- 570
- References
- 1
- Citations by year
- 2026: 18 · 2025: 16 · 2024: 22 · 2023: 27 · 2022: 29 · 2021: 57 · 2020: 82 · 2019: 44 · 2018: 14 · 2017: 11 · 2016: 9 · 2015: 12 · 2014: 7 · 2013: 10 · 2012: 22
- FWCI
- 9.89
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the ACM (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- automatic indexing, document classification, subject classification, clue words, statistical classification, subject categories, information retrieval
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