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Arabic Text Categorization Algorithm using Vector Evaluation Method

Paper · 2014 · Ashraf Odeh, Aymen Abu-Errub, Qusai Shambour et al.

Summary

The paper addresses automatic topic assignment for Arabic documents, an area the authors note has seen relatively little research compared with English. They propose a simple pipeline that they call a "vector evaluation" method. Stop words are removed using a 162-entry Arabic list. Remaining words are normalized by stripping punctuation and digits and unifying hamza and taa marbuta variants, then stemmed to strip affixes. Each word of a test document is weighted with classic tf·idf. Only the two highest-weighted words are kept as the document's keywords. These are matched against keyword sets for each category, and the category with the best match percentage wins. The authors built a training collection of 982 Arabic articles across eleven topics and tested on 1000 documents. They report illustrative match scores and claim category-level precision of roughly 98% and 93%. However, the metrics and baselines are not clearly specified. The work matters mainly as a lightweight, transparent baseline for Arabic topic classification.

Key points

  • The reference corpus holds 38,081 word tokens, which reduce to 8,112 distinct words after duplicates are removed.
  • Preprocessing has three parts:
    • a 162-word Arabic stop list;
    • orthographic normalization (several alif/hamza forms mapped to bare alif, taa marbuta mapped to haa);
    • affix-stripping stemming.
  • The weighting formula is W = tf × log(n/df). Only the top two weighted terms represent a test document.
  • There are eleven categories in total. They cover Politics, Economics and Business; Agriculture and Environment; Astronomy and Computer; History and Religion; plus Sport and Tourism, with 982 training documents and 1000 test documents.
  • Example outputs show graded results:
    • document 1 matches Environment at 93.7%;
    • document 396 scores Tourism 86.4% and Business 85.3%;
    • document 741 scores Agriculture 89% but Business 0%.
  • No comparison against other classifiers is actually reported.

Interesting ideas

  • I1. Collapsing spelling variants (hamza seats, final taa forms) into one canonical letter before counting removes noisy orthographic splits cheaply and predictably.
  • I2. Instead of a single label, each document gets a match percentage per category, which exposes genuinely ambiguous texts (Tourism vs. Business at nearly equal scores).
  • I3. Representing a document by only its two most distinctive terms is an extreme but very interpretable compression: a human can see exactly why the label was chosen.
  • I4. A related method described in the paper first decides a coarse category with tf·idf and then a subcategory with a chi-square measure, splitting a hard decision into two simpler ones.
  • I5. The paper offers a cautionary lesson rather than a technique: headline precision is reported without baselines, confusion data or a clear metric definition, so its claims cannot be checked.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (tokens): A comparable explicit, rule-listed normalization layer for English (curly vs. straight quotes, ligatures, British/American spellings) could feed the lemmatizer cleaner input while staying fully explainable.
  • I2 (answer): When a reader must choose among options, keeping and surfacing the near-tie scores would make uncertain answers visible and explainable rather than silently forced.
  • I3 (world): A tiny tf·idf-picked set of salient lemmas could act as a cheap, auditable domain hint for choosing which ontology slice or verb-class knowledge to load, without replacing tree-based meaning.
  • I4 (world): Coarse-to-fine lookup (domain first, then fine WordNet/Wikidata sense within it) could shrink ambiguity in entity and sense resolution.
  • I5 (learn): Any evaluation should include a trivial baseline and negative controls alongside the headline number, so improvements are demonstrably real.

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

🎓University of JordanAmman · Jordan14 works · 171 citations🎓Al-Ahliyya Amman UniversityAmman · Jordan4 works · 33 citations

Also on the byline: Isra University.

 Fields, topics and concepts

📚 Shelves 4The 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 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2,908 worksEvaluation & benchmarks · 1,220 works

Cite

Cite as Ashraf Odeh, Aymen Abu-Errub, Qusai Shambour and Nidal Turab. 2014. Arabic Text Categorization Algorithm using Vector Evaluation Method. arXiv:1501.01318 · International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 6, December 2014.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.5121/ijcsit.2014.6606 · arXiv 1501.01318

@misc{odeh2014arabic,
  title = {{Arabic Text Categorization Algorithm using Vector Evaluation Method}},
  author = {Ashraf Odeh, Aymen Abu-Errub, Qusai Shambour and Nidal Turab},
  year = {2014},
  doi = {10.5121/ijcsit.2014.6606},
  eprint = {1501.01318},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.5121/ijcsit.2014.6606}
}

References

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