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# An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation

**Authors:** [Meni Adler](https://md.mova.elamur.ai/free/en/docs/papers/authors/meni-adler/); [Michael Elhadad](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-elhadad/) · **Year:** 2006 · **Venue:** Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pp. 665–672 **Link:** [aclanthology.org/P06-1084](https://aclanthology.org/P06-1084/) · library `aclbulk-2006-adler-unsupervised-morpheme-based-hmm-hebrew` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**61** citations](https://openalex.org/works?filter=cites:W2163786648) openalex · 2026-10-19[**38** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2006-adler-unsupervised-morpheme-based-hmm-hebrew/) of the 140673 papers whose bibliographies we read[Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/P06-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P06-1084/)
- [PDF](https://aclanthology.org/P06-1084.pdf)
- [DOI 10.3115/1220175.1220259](https://doi.org/10.3115/1220175.1220259)
- [OpenAlex](https://openalex.org/W2163786648)

**Authors (2)**

1. [Meni Adler](https://openalex.org/A5009922010) [Semantic Scholar](https://www.semanticscholar.org/author/2819757) · h 11 · 303 citations · 21 works
2. [Michael Elhadad](https://openalex.org/A5013658527) [ORCID](https://orcid.org/0000-0002-5629-2351) · [Semantic Scholar](https://www.semanticscholar.org/author/1754680) · h 25 · 3695 citations · 137 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

Adler and Elhadad disambiguate Hebrew morphology with an unsupervised HMM. The only resource is a morphological analyser, with no tagged corpus. Because the morphemes of a word cannot be seen, only the words they form, each morpheme slot in the sentence is a lattice node that records its possible predecessors and successors. They adapt Baum-Welch and Viterbi to this lattice, so segmentation and tagging are learned in one step. The data are about 6M words of newspaper text; the average word has 2.4 analyses, and the full tag set has 1,934 tags against about 200 morpheme tags. The test set is about 30K hand-annotated words. The morpheme model with a context-free initialisation reaches 88.5% for full morphological disambiguation, against 85.75% for the word model and 78.2% for the baseline. For segmentation plus POS it reaches 92.32%. Unknown words (7.5% of the test set) and the noun, verb and adjective confusions of participles account for most errors.

## Ideas

- I1. Keep all segmentations of a token as alternatives in a lattice, and let a Viterbi pass choose the path, instead of committing to the segmenter's best answer first. For `tools/hbo`, this would sit between the segmenter and `gparse`; the lattice is deterministic and can be checked by hand.
- I2. Initial probabilities from a context-free approximation: similar-word statistics (the paper's Levinger-style prior) lift the word model from 82.01 to 84.08 with a first-order HMM. Measure this prior for the Hebrew lexicon before any model is trained on it.
- I3. The paper's confusion table is a checklist of error classes: proper name against noun (17.9%), noun against verb (15.3%), adjective against noun (5.4%), all from the participle. Write these as test cases in [05. The verb: system, binyanim, tense and use](https://md.mova.elamur.ai/free/en/docs/languages/he/05-verbs/) and [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/).
- I4. A fallback for unknown tokens: enumerate all segmentations with affix tags, give the unanalysed base form a single tag UK, and set its tag from the corpus distribution of similar forms. This is a rule to try in `tools/hbo` for tokens the lexicon misses.

## For Mova

I1 goes into `tools/hbo`, between the segmenter and `gparse`. I2 is a measured prior for the `he` lexicon, recorded in [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/). I3 is a set of test cases for [05. The verb: system, binyanim, tense and use](https://md.mova.elamur.ai/free/en/docs/languages/he/05-verbs/) and [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/). I4 is a fallback rule for `tools/hbo`. The model is not used; the lattice and the error classes are what carry over.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1220175.1220259](https://doi.org/10.3115/1220175.1220259)

```
@inproceedings{adler2006unsupervised,
  title = {{An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation}},
  author = {Adler, Meni and Elhadad, Michael},
  booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics},
  year = {2006},
  doi = {10.3115/1220175.1220259},
  url = {https://doi.org/10.3115/1220175.1220259}
}
```

- [← previous Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos/)
- [next → Noun Phrase Chunking in Hebrew: Influence of Lexical and Morphological Features](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-goldberg-noun-phrase-chunking-hebrew-influence/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

## References

23 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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2. Roy Bar-Haim, Khalil Sima’an, and Yoad Winter. 2005. Choosing an optimal architecture for segmentation and pos-tagging of modern Hebrew. In Proceedings of ACL-05 Workshop on Computational Approaches to Semitic Languages. [find](https://scholar.google.com/scholar?q=Roy+Bar%2DHaim%2C+Khalil+Sima%E2%80%99an%2C+and+Yoad+Winter%2E+2005%2E+Choosing+an+optimal+architecture+for+segmentation+and+pos%2Dtagging+of+modern+Hebrew%2E+In+Proceedings+of+ACL%2D05+Workshop+on+Computational+Approaches+t) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos/)
3. Leonard E. Baum. 1972. An inequality and associated maximization technique in statistical estimation for probabilistic functions of a Markov process. Inequalities, 3:1–8. [find](https://scholar.google.com/scholar?q=Leonard+E%2E+Baum%2E+1972%2E+An+inequality+and+associated+maximization+technique+in+statistical+estimation+for+probabilistic+functions+of+a+Markov+process%2E+Inequalities%2C+3%3A1%E2%80%938%2E)
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7. Stanley F. Chen. 1996. Building Probabilistic Models for Natural Language. Ph.D. thesis, Harvard University, Cambridge, MA. [find](https://scholar.google.com/scholar?q=Stanley+F%2E+Chen%2E+1996%2E+Building+Probabilistic+Models+for+Natural+Language%2E+Ph%2ED%2E+thesis%2C+Harvard+University%2C+Cambridge%2C+MA%2E)
8. Mona Diab, Kadri Hacioglu, and Daniel Jurafsky. 2004. Automatic tagging of Arabic text: From raw text to base phrase chunks. In Proceeding of HLT-NAACL-04. [find](https://scholar.google.com/scholar?q=Mona+Diab%2C+Kadri+Hacioglu%2C+and+Daniel+Jurafsky%2E+2004%2E+Automatic+tagging+of+Arabic+text%3A+From+raw+text+to+base+phrase+chunks%2E+In+Proceeding+of+HLT%2DNAACL%2D04%2E)
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11. David Elworthy. 1994. Does Baum-Welch re-estimation help taggers? In Proceeding of ANLP-94. [find](https://scholar.google.com/scholar?q=David+Elworthy%2E+1994%2E+Does+Baum%2DWelch+re%2Destimation+help+taggers%3F+In+Proceeding+of+ANLP%2D94%2E)
12. Nizar Habash and Owen Rambow. 2005. Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In Proceeding of ACL-05. [find](https://scholar.google.com/scholar?q=Nizar+Habash+and+Owen+Rambow%2E+2005%2E+Arabic+tokenization%2C+part%2Dof%2Dspeech+tagging+and+morphological+disambiguation+in+one+fell+swoop%2E+In+Proceeding+of+ACL%2D05%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-habash-arabic-tokenization-part-speech-tagging/)
13. Nadav Har’el and Dan Kenigsberg. 2004. HSpell - the free Hebrew spell checker and morphological analyzer. Israeli Seminar on Computational Linguistics, December 2004. [find](https://scholar.google.com/scholar?q=Nadav+Har%E2%80%99el+and+Dan+Kenigsberg%2E+2004%2E+HSpell+%2D+the+free+Hebrew+spell+checker+and+morphological+analyzer%2E+Israeli+Seminar+on+Computational+Linguistics%2C+December+2004%2E)
14. Daniel Jurafsky and James H. Martin. 2000. Speech and language processing. Prentice-Hall. [find](https://scholar.google.com/scholar?q=Daniel+Jurafsky+and+James+H%2E+Martin%2E+2000%2E+Speech+and+language+processing%2E+Prentice%2DHall%2E)
15. Gennady Lembersky. 2001. Named entities recognition; compounds: approaches and recognitions methods. Master’s thesis, Ben Gurion University of the Negev, Beer Sheva, Israel. (in Hebrew). [find](https://scholar.google.com/scholar?q=Gennady+Lembersky%2E+2001%2E+Named+entities+recognition%3B+compounds%3A+approaches+and+recognitions+methods%2E+Master%E2%80%99s+thesis%2C+Ben+Gurion+University+of+the+Negev%2C+Beer+Sheva%2C+Israel%2E+%28in+Hebrew%29%2E)
16. Moshe Levinger, Uzi Ornan, and Alon Itai. 1995. Learning morpholexical probabilities from an untagged corpus with an application to Hebrew. Computational Linguistics, 21:383–404. [find](https://scholar.google.com/scholar?q=Moshe+Levinger%2C+Uzi+Ornan%2C+and+Alon+Itai%2E+1995%2E+Learning+morpholexical+probabilities+from+an+untagged+corpus+with+an+application+to+Hebrew%2E+Computational+Linguistics%2C+21%3A383%E2%80%93404%2E)
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