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MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction

Paper · 2018 · Ossama Obeid, Salam Khalifa, Nizar Habash et al.

Summary

MADARi is a web-based tool for annotating social-media Arabic, which is heavily dialectal and full of spelling errors. Earlier Arabic annotation tools handled spelling correction, POS tagging and diacritization separately. Chaining those tools lets mistakes from one layer leak into the next. MADARi instead lets annotators fix spelling and annotate morphology in one interface, switching freely between the two. The morphology layer covers proclitics, baseword and enclitics, plus POS tags, features, lemma, English gloss and dialect label. Spelling is normalized to the CODA conventions. MADAMIRA pre-fills every field so annotators mostly confirm or adjust its suggestions. The tool can also query a multi-dialect analyzer and show previously submitted analyses. A separate management interface lets a lead annotator assign, track and grade documents remotely across many annotators. The architecture reuses MANDIAC's client-server design and hybrid SQL/JSON storage, but stores data per sentence so that large files stay fast. The authors also report a user study with the system. The work matters because it supports building dialectal Arabic morphological corpora for the MADAR project.

Key points

  • The tool is named after the MADAR project (Multi-Arabic Dialect Annotations and Resources). It annotates tokenization, POS, lemma, gloss and dialect, using the CAMEL POS tag set and features.
  • Spelling correction means conventionalizing dialectal text to the CODA standard. Edits are token-based, an approach inherited from QAWI: arrow buttons merge a token with its neighbour, and inserting spaces in a pop-up splits it.
  • Its design borrows from three earlier tools:
    • DIWAN contributed pre-computed analyses and applying one analysis to many occurrences.
    • QAWI contributed exact recording of every edit.
    • MANDIAC contributed the extensible SQL/JSON storage.
  • Documents are stored one sentence per row rather than one document per row. Saving therefore rewrites only the JSON of the sentences that changed.
  • MADAMIRA pre-computes the analyses. Built-in rewrite rules handle simple detokenization, and annotators can override the surface form by hand (e.g., w+ jAbwA +hA → wjAbwhA).

Interesting ideas

  • I1. Doing spelling correction and morphological disambiguation in one place, with free back-and-forth, keeps a wrong early decision from silently corrupting later layers.
  • I2. Splitting a word into clitics is not simply the reverse of joining them, because orthographic rewrite rules apply at the boundaries; MADARi keeps both the segmented form and an explicit detokenized form, auto-generated by rules but editable.
  • I3. Pre-filling every field with an automatic analyzer's output turns annotation into verification, and a per-word "validated" flag separates confirmed items from untouched suggestions.
  • I4. Showing all occurrences of the same written form in the document and letting one analysis apply to many speeds up annotation and enforces consistency.
  • I5. Storing data per sentence with a flexible JSON payload lets new annotation layers be added with almost no back-end change and keeps saves cheap.

For Mova

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

  • I1 (tokens): Let the tokenizer, spelling-normalizer and tagger revise each other on noisy input instead of running as a fixed pipeline, and record which joint decision was made so it stays explainable.
  • I2 (back): When generating text from the graph, store contraction and boundary rules explicitly (e.g., "do + n't", "a/an") as a reversible rule table rather than plain concatenation.
  • I3 (learn): In silver data, carry a per-token flag distinguishing witness-parser defaults from LLM-teacher or human-confirmed labels, so training and evaluation can weight or exclude unverified items.
  • I4 (learn): Group repeated word forms or constructions across silver data and review them as a batch, so a single repair rule fixes every instance consistently.
  • I5 (world): Store per-sentence analyses (tree, frames, world deltas) as independent extensible records, so new layers can be added and single sentences re-analysed without rewriting the whole document.

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

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓New York UniversityNew York · United States201 works · 100,180 citations🎓Hamad bin Khalifa UniversityDoha · Qatar141 works · 14,175 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates122 works · 4,602 citations🎓Carnegie Mellon University QatarDoha · Qatar47 works · 2,773 citations

 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 3Research subjects and languages the work is about.Semitic languages · 2,908 worksMorphology · 405 worksGrammatical error correction & learner language · 156 works

Cite

Cite as Ossama Obeid, Salam Khalifa, Nizar Habash, Houda Bouamor, Wajdi Zaghouani and Kemal Oflazer. 2018. MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{obeid2018madari,
  title = {{MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction}},
  author = {Ossama Obeid, Salam Khalifa, Nizar Habash, Houda Bouamor, Wajdi Zaghouani and Kemal Oflazer},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/5nejcfwotfzc},
  url = {https://doi.org/10.63317/5nejcfwotfzc}
}

References

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