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Dotless Representation of Arabic Text: Analysis and Modeling

Summary

Arabic script has many letters that share a base shape (rasm) and differ only in their dots. The authors ask what happens if the dots are removed entirely, so that dotted words collapse into shared dotless homographs. They compare dotless and standard Arabic across five corpora of different domains and sizes and four tokenization schemes, including characters, words, farasa segments and disjoint letters. For each setting they look at vocabulary size, Zipf's and Heap's laws and text entropy. They then train statistical n-gram and neural language models on both representations and compare perplexity. A parallel English–Arabic comparison on UN and Wikipedia data rounds out the study.

Dropping dots shrinks vocabularies and character sets considerably, yet entropy falls only slightly. With longer context or finer tokens, dotless perplexity approaches dotted perplexity. This suggests contextual models can recover much of what the dots encode. The finding is relevant to:

  • OCR of ancient dotless manuscripts,
  • social-media posts that strip dots to evade moderation,
  • shrinking Arabic NLP vocabularies.

Key points

  • On the aggregated dataset, dotless word vocabulary is 68.1% of the dotted one, and the reduction grows with dataset richness. Disjoint-letters tokenization gives the largest cut, about 60%.
  • At character level, dotless text needs a character set 61.3% smaller, while its entropy stays within 10% of dotted text. The dots account for only about 5% of redundancy.
  • Every tokenization follows Zipf's law, and coarser units are more Zipfian. Both representations obey Heap's law, but dotless vocabulary grows more slowly, especially on rich corpora.
  • In n-gram models, the dotless–dotted perplexity gap narrows as n grows, more so for finer tokenization. Neural character-level models perform almost identically on the two representations.
  • Dotless text yields smaller neural models through smaller vocabularies, except for some farasa settings where the chosen vocabulary size prevented a reduction. On parallel data, English word entropy is roughly one-third below Arabic.

Interesting ideas

  • I1. Treat a deliberately lossy, many-to-one normalization as a first-class alternative representation, then quantify its cost with entropy and its gain with vocabulary shrinkage. — For Mova (tokens): Candidate collapses in crate en (case folding, spelling variants, lemma-only views) could be scored this way before adoption, keeping only those that save much vocabulary for little information.
  • I2. Measure how the gap between the lossy and full representation shrinks as more context is available (higher n-gram order). This estimates how much context is needed to undo the ambiguity. — For Mova (learn): A similar context-length curve could test how much surrounding text the tagger or parser needs to resolve homographs, giving an explainable ambiguity metric.
  • I3. Use entropy differences to assign a redundancy share to one specific script feature, showing that other redundancy sources remain. — For Mova (tokens): Comparing entropy with and without features such as punctuation or capitalization would show which surface cues actually carry information for tokenization and tagging.
  • I4. Compare Heap's-law growth curves across representations and corpus richness instead of a single vocabulary count, since the advantage depends on data diversity. — For Mova (learn): When building silver treebanks, growth curves of lemma and frame vocabularies could reveal whether new data still adds diversity or just repeats.
  • I5. Run the same frequency and entropy analyses on both sides of a parallel corpus to see that cross-language vocabulary differences are uneven across frequency bands. — For Mova (back): The text → graph → text compressor could be checked on parallel texts, comparing the information profile of the meaning graph against each language's surface.

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

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2312.16104 · arXiv 2312.16104

@misc{alshaibani2023dotless,
  title = {{Dotless Representation of Arabic Text: Analysis and Modeling}},
  author = {Al-Shaibani, Maged S. and Ahmad, Irfan},
  year = {2023},
  doi = {10.48550/arxiv.2312.16104},
  eprint = {2312.16104},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.16104}
}

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