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Machine Learning in Automated Text Categorization

Paper · 2002 · Fabrizio Sebastiani

Summary

This survey covers automated text categorization (TC), the task of assigning documents to thematic categories from a predefined set. Sebastiani argues that the field moved during the 1990s from hand-written expert rules (knowledge engineering) to machine learning. In the ML approach, a generic inductive procedure builds a classifier from a collection of documents that are already labelled. The paper frames TC as approximating an unknown target function over document–category pairs. It is organized around three problems: turning texts into representations a learner can use, building the classifier, and measuring its effectiveness. It also separates several task variants: single-label versus multi-label, and document-pivoted versus category-pivoted use. The main conclusion is that learned classifiers now perform about as well as trained human indexers, at much lower labour cost, and can be moved to new category sets easily. Theoretically grounded learners such as SVMs and boosting drove these gains. The survey matters because it gave a scattered literature one systematic reference, with no textbook-level treatment available at the time.

Key points

  • The paper defines TC as assigning a Boolean value (T/F) to each pair (document, category). Categories are treated as bare symbolic labels, and only endogenous knowledge (the text itself, with no metadata) is assumed.
  • A multi-label problem over |C| categories can be split into |C| independent binary problems, provided the categories are stochastically independent. The reverse reduction does not hold, so the survey focuses on the binary case.
  • Humans do not agree perfectly either (inter-indexer inconsistency). For example, a news story about Clinton at Dizzy Gillespie's funeral could plausibly go under Politics, Jazz, both, or neither.
  • Stronger methods are credited for the effectiveness gains since the early '90s:
    • multiplicative weight updating (Winnow, Widrow-Hoff),
    • boosting,
    • support vector machines. These are contrasted with the weaker Rocchio method.
  • TC methods have also been applied to noisy OCR output (Ittner et al. 1995) and to speech transcripts. One speech example is Schapire and Singer (2000), who routed phone calls by classifying callers' answers.

Interesting ideas

  • I1. Train on the same noise you will see at test time. Ittner et al. found that training on OCR-corrupted text gave results close to those on clean text. This could carry over to noisy Ukrainian sources such as scanned archives, ASR output, or social media.
  • I2. Decompose multi-label tasks into per-category binary classifiers. This keeps the modelling simple and makes it easy to add a new category later. The cost is an assumption that categories are independent, which should be checked.
  • I3. Pick document-pivoted or category-pivoted workflows deliberately. Document-pivoted classification suits streams of incoming items, such as filtering. Category-pivoted classification suits cases where a new label is added and the existing archive must be re-scanned. This choice affects pipeline design for data or trade catalogues.
  • I4. Include a no-learning baseline. Yang's Word/STR baseline simply matches documents against category names. It measures how much the learning component actually adds, which is a cheap sanity check for any new classifier.
  • I5. Use significance tests in model comparisons. Following Yang and Liu (1999), apply tests suited to both micro- and macro-averaged scores rather than relying on raw differences. This gives more trustworthy model rankings, especially when categories are unevenly populated.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🤝National Research CouncilRome · Italy15 works · 15,649 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksGeneral Social Sciences · 227 works

💡 Concepts 2Key ideas and methods explained on our concept pages.Machine learning · 51 worksText classification · 50 works

Cite

Cite as Fabrizio Sebastiani. 2002. Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/505282.505283 · arXiv cs/0110053

@inproceedings{sebastiani2002machine,
  title = {{Machine Learning in Automated Text Categorization}},
  author = {Fabrizio Sebastiani},
  booktitle = {ACM Computing Surveys 34(1)},
  year = {2002},
  doi = {10.1145/505282.505283},
  eprint = {cs/0110053},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/505282.505283}
}

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