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# Integrated Morphological and Syntactic Disambiguation for Modern Hebrew

**Authors:** [Reut Tsarfaty](https://md.mova.elamur.ai/free/en/docs/papers/authors/reut-tsarfaty/) · **Year:** 2006 · **Venue:** Proceedings of the COLING/ACL 2006 Student Research Workshop **Link:** [aclanthology.org/P06-3009](https://aclanthology.org/P06-3009/) · library `aclbulk-2006-tsarfaty-integrated-morphological-syntactic-disambiguation-modern` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**38** citations](https://openalex.org/works?filter=cites:W2113024997) openalex · 2026-10-18[**29** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2006-tsarfaty-integrated-morphological-syntactic-disambiguation-modern/) of the 140673 papers whose bibliographies we read[Proceedings of the COLING/ACL 2006 Student Research Workshop](https://aclanthology.org/volumes/P06-3/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P06-3009/)
- [PDF](https://aclanthology.org/P06-3009.pdf)
- [DOI 10.3115/1557856.1557867](https://doi.org/10.3115/1557856.1557867)
- [OpenAlex](https://openalex.org/W2113024997)

**Authors (1)**

1. [Tsarfaty, Reut](https://openalex.org/A5063283689) [ORCID](https://orcid.org/0000-0001-6406-6568) · h 24 · 2886 citations · 217 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Hebrew morphology (prefixed particles, clitics, a fused word that may span several phrases) makes word-level parsing hard. The paper builds the first statistical parser for Modern Hebrew that does not need manual morphological disambiguation. It runs three stages: a morphological analyser proposes segmentations, a HMM tagger assigns POS tags to segments, and a PCFG parser builds trees over the segments. Model I passes only the chosen segmentation to the parser; Model II passes the tagged segments, so that the lowest non-terminals carry the morphology. The treebank has about 3,250 sentences of length 2 to 20. Labeled precision and recall are about 60% for both models, Model II gains up to about 2 points, and the string coverage falls from 99.2% to 95.9%. Scores are measured on character spans, because the constituents do not follow word boundaries.

## Ideas

- I1. Let the parser see the tagged segments, not only the chosen segmentation: keep the segmentation and tag choice as a lattice and pass the best tag per segment to a parser. Measure on the same split to see whether labeled scores go up at a cost in coverage.
- I2. Compare tag sets on the parse score, not on the tag score: adding finite and non-finite verb forms lowered tagging accuracy but raised parsing accuracy. Test a finite/non-finite split in the [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/) tag mapping by parse F.
- I3. Score constituents as character spans (start, end, label) rather than as morpheme or word lists, so that a span across word boundaries is scored correctly. Add this span evaluation to `tools/macula compare` for Hebrew parses.

## For Mova

I1 is a design note for the segment stage of `tools/hbo` (lattice into the parser; `gparse` takes segment tags). I2 is a test on the tag mapping in [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/). I3 goes into the evaluation of `tools/macula compare` and [Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/). The 2006 system is a baseline only; the data is small, and Mova's own parser is neural-free.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1557856.1557867](https://doi.org/10.3115/1557856.1557867)

```
@inproceedings{tsarfaty2006integrated,
  title = {{Integrated Morphological and Syntactic Disambiguation for Modern Hebrew}},
  author = {Tsarfaty, Reut},
  booktitle = {Proceedings of the COLING/ACL 2006 Student Research Workshop},
  year = {2006},
  doi = {10.3115/1557856.1557867},
  url = {https://doi.org/10.3115/1557856.1557867}
}
```

- [← previous 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/)
- [next → SVM Model Tampering and Anchored Learning: A Case Study in Hebrew NP Chunking](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2007-goldberg-svm-model-tampering-anchored-learning/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

## References

9 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Meni Adler and Dudi Gabai. 2005. Morphological Analyzer and Disambiguator for Modern Hebrew. Knowledge Center for Processing Hebrew. [find](https://scholar.google.com/scholar?q=Meni+Adler+and+Dudi+Gabai%2E+2005%2E+Morphological+Analyzer+and+Disambiguator+for+Modern+Hebrew%2E+Knowledge+Center+for+Processing+Hebrew%2E)
2. Roy Bar-Haim. 2005. Part-of-Speech Tagging for Hebrew and Other Semitic Languages. Master’s thesis, Technion, Haifa, Israel. [find](https://scholar.google.com/scholar?q=Roy+Bar%2DHaim%2E+2005%2E+Part%2Dof%2DSpeech+Tagging+for+Hebrew+and+Other+Semitic+Languages%2E+Master%E2%80%99s+thesis%2C+Technion%2C+Haifa%2C+Israel%2E)
3. Eugene Charniak, Glenn Carroll, John Adcock, Anthony R. Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael L. Littman, and John McCann. 1996. Taggers for Parsers. AI, 85(1-2):45–57. [find](https://scholar.google.com/scholar?q=Eugene+Charniak%2C+Glenn+Carroll%2C+John+Adcock%2C+Anthony+R%2E+Cassandra%2C+Yoshihiko+Gotoh%2C+Jeremy+Katz%2C+Michael+L%2E+Littman%2C+and+John+McCann%2E+1996%2E+Taggers+for+Parsers%2E+AI%2C+85%281%2D2%29%3A45%E2%80%9357%2E)
4. Eugene Charniak. 2000. A Maximum-Entropy-Inspired Parser. In Proceedings of NAACL 2000. [find](https://scholar.google.com/scholar?q=Eugene+Charniak%2E+2000%2E+A+Maximum%2DEntropy%2DInspired+Parser%2E+In+Proceedings+of+NAACL+2000%2E)
5. Michael Collins. 1997. Three Generative, Lexicalised Models for Statistical Parsing. In Proceedings of ACL-EACL 1997. [find](https://scholar.google.com/scholar?q=Michael+Collins%2E+1997%2E+Three+Generative%2C+Lexicalised+Models+for+Statistical+Parsing%2E+In+Proceedings+of+ACL%2DEACL+1997%2E)
6. Nizar Habash and Owen Rambow. 2005. Arabic Tokenization, Part-of-Speech Tagging and Morphological Disambiguation in One Fell Swoop. In Proceedings of ACL 2005. [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+Proceedings+of+ACL+2005%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-habash-arabic-tokenization-part-speech-tagging/)
7. Helmut Schmid, 2000. LoPar: Design and Implementation. Institute for Computational Linguistics, University of Stuttgart. [find](https://scholar.google.com/scholar?q=Helmut+Schmid%2C+2000%2E+LoPar%3A+Design+and+Implementation%2E+Institute+for+Computational+Linguistics%2C+University+of+Stuttgart%2E)
8. Erel Segal. 2000. A Probabilistic Morphological Analyzer for Hebrew Undotted Texts. Master’s thesis, Computer Science Department, Technion, Isreal. [find](https://scholar.google.com/scholar?q=Erel+Segal%2E+2000%2E+A+Probabilistic+Morphological+Analyzer+for+Hebrew+Undotted+Texts%2E+Master%E2%80%99s+thesis%2C+Computer+Science+Department%2C+Technion%2C+Isreal%2E)
9. Khalil Sima’an, Alon Itai, Yoad Winter, Alon Altman, and Noa Nativ. 2001. Building a Tree-Bank for Modern Hebrew Text. In Traitement Automatique des Langues, volume 42, pages 347–380. [find](https://scholar.google.com/scholar?q=Khalil+Sima%E2%80%99an%2C+Alon+Itai%2C+Yoad+Winter%2C+Alon+Altman%2C+and+Noa+Nativ%2E+2001%2E+Building+a+Tree%2DBank+for+Modern+Hebrew+Text%2E+In+Traitement+Automatique+des+Langues%2C+volume+42%2C+pages+347%E2%80%93380%2E)