---
title: "An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation — Adler 2006"
description: "Adler and Elhadad disambiguate Hebrew morphology with an unsupervised HMM. 23 references, cited by 38 papers here."
authors: ["Meni Adler", "Michael Elhadad"]
year: 2006
venue: "Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P06-1084/"
doi: "10.3115/1220175.1220259"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/adler-2006-unsupervised-morpheme-based-hmm-hebrew/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/adler-2006-unsupervised-morpheme-based-hmm-hebrew/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2006](https://md.research.elamur.ai/free/en/papers/2006/) / An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation

# An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation

**Authors:** [Meni Adler](https://md.research.elamur.ai/free/en/authors/meni-adler/); [Michael Elhadad](https://md.research.elamur.ai/free/en/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, 2026-10-14 · [**38** citations in our library](https://md.research.elamur.ai/free/en/papers/adler-2006-unsupervised-morpheme-based-hmm-hebrew/cited-by/) of the 140,884 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 · 3,571 citations · 137 works

Citations and author metrics: OpenAlex, 2026-10-14; 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

- 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.

## For Mova

- 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.
- 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 [languages/he/05-verbs.md](https://mova.elamur.ai/free/en/docs/languages/he/05-verbs/) and [languages/he/04-nouns-adjectives-pronouns.md](https://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.

I1 goes into `tools/hbo`, between the segmenter and `gparse`. I2 is a measured prior for the `he` lexicon, recorded in [languages/he/11-ud-analysis.md](https://mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/). I3 is a set of test cases for [languages/he/05-verbs.md](https://mova.elamur.ai/free/en/docs/languages/he/05-verbs/) and [languages/he/04-nouns-adjectives-pronouns.md](https://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.

## Institutions

[🎓**Ben-Gurion University of the Negev** Beersheba · Israel 34 works · 2,748 citations](https://md.research.elamur.ai/free/en/universities/ben-gurion-university-of-the-negev/)

Topics[Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

Cite as Meni Adler and Michael Elhadad. 2006. An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics.

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)

```bibtex
@inproceedings{adler2006unsupervised,
  title = {{An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation}},
  author = {Meni Adler and Michael Elhadad},
  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 Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge](https://md.research.elamur.ai/free/en/papers/yang-2006-kernel-based-pronoun-resolution-structured/)
- [next → Partially Supervised Coreference Resolution for Opinion Summarization through Structured Rule Learning](https://md.research.elamur.ai/free/en/papers/stoyanov-2006-partially-supervised-coreference-resolution-opinion/)
- [↑ back to papers of 2006](https://md.research.elamur.ai/free/en/papers/2006/)

## 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.

1. Emmanuel Allon. 1995. Unvocalized Hebrew Writing. Ben Gurion University Press. (in Hebrew). [find](https://scholar.google.com/scholar?q=Emmanuel+Allon%2E+1995%2E+Unvocalized+Hebrew+Writing%2E+Ben+Gurion+University+Press%2E+%28in+Hebrew%29%2E)
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. [our card](https://md.research.elamur.ai/free/en/papers/haim-2005-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)
4. Na’ama Ben-Mordechai. 2005. Named entities recognition in Hebrew. Master’s thesis, Ben Gurion University of the Negev, Beer Sheva, Israel. (in Hebrew). [find](https://scholar.google.com/scholar?q=Na%E2%80%99ama+Ben%2DMordechai%2E+2005%2E+Named+entities+recognition+in+Hebrew%2E+Master%E2%80%99s+thesis%2C+Ben+Gurion+University+of+the+Negev%2C+Beer+Sheva%2C+Israel%2E+%28in+Hebrew%29%2E)
5. Eric Brill. 1995. Transformation-based error-driven learning and natural languge processing: A case study in part-of-speech tagging. Computational Linguistics, 21:543–565. [find](https://scholar.google.com/scholar?q=Eric+Brill%2E+1995%2E+Transformation%2Dbased+error%2Ddriven+learning+and+natural+languge+processing%3A+A+case+study+in+part%2Dof%2Dspeech+tagging%2E+Computational+Linguistics%2C+21%3A543%E2%80%93565%2E)
6. David Carmel and Yoelle S. Maarek. 1999. Morphological disambiguation for Hebrew search systems. In Proceeding of NGITS-99. [find](https://scholar.google.com/scholar?q=David+Carmel+and+Yoelle+S%2E+Maarek%2E+1999%2E+Morphological+disambiguation+for+Hebrew+search+systems%2E+In+Proceeding+of+NGITS%2D99%2E)
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)
9. Kevin Duh and Katrin Kirchhoff. 2005. Pos tagging of dialectal Arabic: A minimally supervised approach. In Proceedings of ACL-05 Workshop on Computational Approaches to Semitic Languages. [find](https://scholar.google.com/scholar?q=Kevin+Duh+and+Katrin+Kirchhoff%2E+2005%2E+Pos+tagging+of+dialectal+Arabic%3A+A+minimally+supervised+approach%2E+In+Proceedings+of+ACL%2D05+Workshop+on+Computational+Approaches+to+Semitic+Languages%2E)
10. Michael Elhadad, Yael Netzer, David Gabay, and Meni Adler. 2005. Hebrew morphological tagging guidelines. Technical report, Ben Gurion University, Dept. of Computer Science. [find](https://scholar.google.com/scholar?q=Michael+Elhadad%2C+Yael+Netzer%2C+David+Gabay%2C+and+Meni+Adler%2E+2005%2E+Hebrew+morphological+tagging+guidelines%2E+Technical+report%2C+Ben+Gurion+University%2C+Dept%2E+of+Computer+Science%2E)
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. [our card](https://md.research.elamur.ai/free/en/papers/habash-2005-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)
17. Moshe Levinger. 1992. Morhphological disambiguation in hebrew. Master’s thesis, Technion, Haifa, Israel. (in Hebrew). [find](https://scholar.google.com/scholar?q=Moshe+Levinger%2E+1992%2E+Morhphological+disambiguation+in+hebrew%2E+Master%E2%80%99s+thesis%2C+Technion%2C+Haifa%2C+Israel%2E+%28in+Hebrew%29%2E)
18. Christopher D. Manning and Hinrich Schutze. 1999. Foundation of Statistical Language Processing. MIT Press. [find](https://scholar.google.com/scholar?q=Christopher+D%2E+Manning+and+Hinrich+Schutze%2E+1999%2E+Foundation+of+Statistical+Language+Processing%2E+MIT+Press%2E)
19. Bernard Merialdo. 1994. Tagging English text with probabilistic model. Computatinal Linguistics, 20:155–171. [find](https://scholar.google.com/scholar?q=Bernard+Merialdo%2E+1994%2E+Tagging+English+text+with+probabilistic+model%2E+Computatinal+Linguistics%2C+20%3A155%E2%80%93171%2E)
20. Uzi Ornan. 2002. Hebrew in latin script. Lĕšonénu, LXIV:137–151. (in Hebrew). [find](https://scholar.google.com/scholar?q=Uzi+Ornan%2E+2002%2E+Hebrew+in+latin+script%2E+Le%CC%86s%CC%8Cone%CC%81nu%2C+LXIV%3A137%E2%80%93151%2E+%28in+Hebrew%29%2E)
21. Erel Segal. 2000. Hebrew morphological analyzer for Hebrew undotted texts. Master’s thesis, Technion, Haifa, Israel. (in Hebrew). [find](https://scholar.google.com/scholar?q=Erel+Segal%2E+2000%2E+Hebrew+morphological+analyzer+for+Hebrew+undotted+texts%2E+Master%E2%80%99s+thesis%2C+Technion%2C+Haifa%2C+Israel%2E+%28in+Hebrew%29%2E)
22. Scott M. Thede and Mary P. Harper. 1999. A second-order hidden Markov model for part-of-speech tagging. In Proceeding of ACL-99. [find](https://scholar.google.com/scholar?q=Scott+M%2E+Thede+and+Mary+P%2E+Harper%2E+1999%2E+A+second%2Dorder+hidden+Markov+model+for+part%2Dof%2Dspeech+tagging%2E+In+Proceeding+of+ACL%2D99%2E)
23. Shlomo Yona and Shuly Wintner. 2005. A finite-state morphological grammar of Hebrew. In Proceedings of ACL-05 Workshop on Computational Approaches to Semitic Languages. [our card](https://md.research.elamur.ai/free/en/papers/yona-2005-finite-state-morphological-grammar-hebrew/)