The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automatic Document Classification
Abstract
Starting with a collection of 405 document abstracts dealing with computers, the experiment in automatic document classification proceeds to construct an empirically based mathematically derived classification system by use of a factor analysis technique. The documents are then classified into these derived categories by five subjects, and the resulting classification serves as a criterion against which the automatic classification is to be evaluated. Of the ninety documents in the Validation Group which contained two or more clue words, and which therefore could be automatically classified, 44 documents, or 48.9%, were placed into their correct categories by use of a computer formula. These results are almost identical to the results obtained by Maron in a previous experiment using the same data but with a different set of classification categories and a different computational formula. The experimental evidence supports the conclusion that automatic document classification is possible. Additional experiments are described which when executed should improve the accuracy of the automatic classification technique. (Author)
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Automatic Indexing: An Experimental Inquiry
Maron, M. E. (1961) · Journal of the ACM · doi:10.1145/321075.321084 · Cited by 570
Cited by
Using latent semantic analysis to improve access to textual information
Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R. (1988) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88 · doi:10.1145/57167.57214 · Cited by 616
Efficient Large-Scale Multi-Modal Classification
Kiela, D., Grave, E., Joulin, A., & Mikolov, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11945 · Cited by 177
Metadata
- Authors
- Borko, H., & Bernick, M.
- Venue
- Journal of the ACM
- Published
- 1963-04
- Volume
- 10
- Issue
- 2
- Pages
- 151-162
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W2107827038
- Cited by
- 187
- References
- 1
- FWCI
- 14.56
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automatic Document Classification. https://app.cochat.ai/papers/10.1145/321160.321165
- The paper
- Borko, H., & Bernick, M. (1963). Automatic Document Classification. Journal of the ACM. https://doi.org/10.1145/321160.321165
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.