📄 Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking

What it is about

MADA picks, for each Arabic word, one analysis from the candidates that the BAMA dictionary proposes. It scores each candidate by summing the weights of the features on which the candidate agrees with the model's prediction. The features are 14 morphological tags, each predicted by an SVM, two checks (spelling match, and whether the analysis is the dictionary default), a unigram model of diacritized forms and of lexemes, and a 4-gram lexeme model. The authors add lemmatization as a task and run seven tasks (tagging, diacritization, lemma choice, and full choice). The weights are tuned by downhill simplex on half of ATB3-DevTest (about 26K words), with the other half (about 25K words) held out for testing. Lexeme features cut error by 36–71% on the diacritic and lemma tasks, and by 3–11% on the morphological tasks. Tuning the weights reduces error further, by 8–20% with all features.

For Mova

• I1. A lexeme n-gram as one vote: a 4-gram model over lemma sequences, which alone picks the right lexeme in 94.1% of dev-test words, is added as one more feature of the candidate score. In Mova the same role can be played by a lemma n-gram table over the segmenter's candidate analyses, built with BTreeMap so the result is deterministic.
• I2. Score candidates as a plain sum of named feature weights, and tune the weights on a split of the training data, one tuning objective per evaluation task. Measure with the two-fold cv check, not the final test, so that the choice of weights is never made on the held-out set.
• I3. Use the agreement-based score as an explanation. Each decision can be printed as the list of features that voted for the winner, which fits Mova's requirement that every step can say why. For unpointed Hebrew this is the diacritic decision of tools/hbo unpointed mode.

I1 goes into the candidate choice of tools/hbo (unpointed mode), where a lexeme table would vote on the segmenter's analyses. I2 is the test design for the same choice (cv split, no peeking at the release test). I3 is the explanation output of that choice. The Arabic data itself belongs to treebanks/ar, and the spelling-match idea is relevant to languages/hbo/01-script.md.

🏛️ Institutions

🎓Columbia UniversityNew York · United States118 works · 44,744 citations

Topics🧩 Morphology🐪 Semitic languages

Cite

Cite as Ryan Roth, Owen Rambow, Nizar Habash, Mona Diab and Cynthia Rudin. 2008. Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. Proceedings of ACL-08: HLT, Short Papers.

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/1557690.1557721

@inproceedings{roth2008arabic,
  title = {{Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking}},
  author = {Ryan Roth, Owen Rambow, Nizar Habash, Mona Diab and Cynthia Rudin},
  booktitle = {Proceedings of ACL-08: HLT, Short Papers},
  year = {2008},
  doi = {10.3115/1557690.1557721},
  url = {https://doi.org/10.3115/1557690.1557721}
}

References

8 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Tim Buckwalter. 2004. Buckwalter Arabic morphological analyzer version 2.0. find
2. Nizar Habash and Owen Rambow. 2005. Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In ACL’05, Ann Arbor, MI, USA. our card
3. Nizar Habash and Owen Rambow. 2007. Arabic diacritization through full morphological tagging. In NAACL HLT 2007 Companion Volume, Short Papers, Rochester, NY, USA. our card
4. Jan Hajič, Otakar Smrž, Tim Buckwalter, and Hubert Jin. 2005. Feature-based tagger of approximations of functional Arabic morphology. In Proceedings of the Workshop on Treebanks and Linguistic Theories (TLT), Barcelona, Spain. find
5. Jan Hajič. 2000. Morphological tagging: Data vs. dictionaries. In 1st Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL’00), Seattle, WA. our card
6. J.A Nelder and R Mead. 1965. A simplex method for function minimization. In Computer Journal, pages 303–333. find
7. Andreas Stolcke. 2002. Srilm - an extensible language toolkit. In Proceedings of the International Conference on Spoken Language Processing (ICSLP). find
8. Imed Zitouni, Jeffrey S. Sorensen, and Ruhi Sarikaya. 2006. Maximum entropy based restoration of arabic diacritics. In Coling-ACL’06, pages 577–584, Sydney, Australia. our card

This page in Markdown → https://md.research.elamur.ai/free/en/papers/roth-2008-arabic-morphological-tagging-diacritization-lemmatization/