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Machine learning in automated text categorization

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Sebastiani, Fabrizio·2002·ACM Computing Surveys
DOI 10.1145/505282.505283
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Abstract

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

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Metadata

Authors
Sebastiani, F.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2002-03
Volume
34
Issue
1
Pages
1-47
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/505282.505283
OpenAlex
W2118020653
Cited by
7,926
References
5
FWCI
227.50
Type
journal article
Language
en
Open access
green
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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 ›

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Machine learning in automated text categorization. https://app.cochat.ai/papers/10.1145/505282.505283
The paper
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys. https://doi.org/10.1145/505282.505283

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.