---
title: "Choosing an Optimal Architecture for — Bar-Haim 2005"
description: "Bar-Haim et al. build an HMM segmenter and POS tagger for Modern Hebrew from a morphological analyser and a very small tagged corpus…"
authors: ["Roy Bar-Haim", "Khalil Sima'an", "Yoad Winter"]
year: 2005
venue: "Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages"
source: "https://aclanthology.org/W05-0706/"
doi: "10.3115/1621787.1621796"
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/aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos/"
---
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# Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew

**Authors:** [Roy Bar-Haim](https://md.research.elamur.ai/free/en/authors/roy-bar-haim/); [Khalil Sima'an](https://md.research.elamur.ai/free/en/authors/khalil-sima-an/); [Yoad Winter](https://md.research.elamur.ai/free/en/authors/yoad-winter/) · **Year:** 2005 · **Venue:** Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, pp. 39–46 **Link:** [aclanthology.org/W05-0706](https://aclanthology.org/W05-0706/) · library `aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**36** citations](https://openalex.org/works?filter=cites:W2154091096) OpenAlex · 2026-10-14 · [**10** citations in our library](https://md.research.elamur.ai/free/en/papers/aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos/cited-by/) of the 140673 papers whose bibliographies we read · [Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages](https://aclanthology.org/volumes/W05-07/) · [licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/W05-0706/)
- [PDF](https://aclanthology.org/W05-0706.pdf)
- [DOI 10.3115/1621787.1621796](https://doi.org/10.3115/1621787.1621796)
- [OpenAlex](https://openalex.org/W2154091096)

**Authors (3)**

1. Roy Bar-Haim
2. [Khalil Sima'an](https://openalex.org/A5034829413) [Semantic Scholar](https://www.semanticscholar.org/author/3540477) · h 23 · 2396 citations · 168 works
3. [Yoad Winter](https://openalex.org/A5070230184) [ORCID](https://orcid.org/0000-0002-7209-710X) · [Semantic Scholar](https://www.semanticscholar.org/author/2021738) · h 28 · 3517 citations · 194 works

Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart.

## What it is about

Bar-Haim et al. build an HMM segmenter and POS tagger for Modern Hebrew from a morphological analyser and a very small tagged corpus (the Hebrew Treebank, from Ha'aretz, with under 30K words per training fold), plus about 340K untagged words. They compare two tokenisations: a word-level model, where each tag is a whole segmentation pattern such as IN-H-NN, and a morpheme-level model, where the tags are plain POS and the input is the segments. The morpheme model tags better (90.21 against 89.42 word-level, 5-fold cross-validation) but segments no better. Error analysis shows that the covert definite article `h` causes many errors: `b-bit` ("in a house") against `b-h-bit` ("in the house"). Their best model, M+h, makes the definiteness marker a feature of the stem's tag and reaches 90.51 tagging and 96.74 segmentation accuracy. A lexical model interpolated with an unsupervised estimate (weight 0.85 on the annotated counts) adds 1.5 points to tagging. The analyser misses 3.78% of test words, which caps accuracy at 96.22%.

## Ideas

- I1. Definiteness as a stem feature: when `b`, `k` or `l` precedes a noun, decide between the bare and the `h`-marked reading from lexical counts of definite and indefinite uses. The paper's example `lmmflh` (25 occurrences, only 2 indefinite) shows the signal. This is a count table for [languages/he/06-prepositions-particles.md](https://mova.elamur.ai/free/en/docs/languages/he/06-prepositions-particles/), with the spelling side in [languages/he/02-script-and-spelling.md](https://mova.elamur.ai/free/en/docs/languages/he/02-script-and-spelling/).
- I2. Interpolate the lexical model: relative frequency from the annotated data with weight 0.85, plus an unsupervised estimate from a large untagged corpus. The same interpolation can be measured on the `lex` table of `tools/hbo`, on held-out text.
- I3. A minimal-pair test: `bbit hgdwl` ("in the big house") against `bbit gdwl` ("in a big house"). The adjective's definiteness decides the segmentation. Use as a test case for Modern Hebrew segmentation in [languages/he/11-ud-analysis.md](https://mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/).

## For Mova

I1 goes into [languages/he/06-prepositions-particles.md](https://mova.elamur.ai/free/en/docs/languages/he/06-prepositions-particles/) and [languages/he/02-script-and-spelling.md](https://mova.elamur.ai/free/en/docs/languages/he/02-script-and-spelling/). I2 is an idea for the `lex` table in `tools/hbo` and for the Modern Hebrew notes in [languages/he/11-ud-analysis.md](https://mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/), to be measured. I3 is a test case for [languages/he/11-ud-analysis.md](https://mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/). The paper's tagger is an HMM; Mova would take the tokenisation choice and the lexical smoothing, not the tagger.

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

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1621787.1621796](https://doi.org/10.3115/1621787.1621796)

```bibtex
@inproceedings{barhaim2005choosing,
  title = {{Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew}},
  author = {Bar-Haim, Roy and Sima'an, Khalil and Winter, Yoad},
  booktitle = {Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages},
  year = {2005},
  doi = {10.3115/1621787.1621796},
  url = {https://doi.org/10.3115/1621787.1621796}
}
```

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- [↑ back to papers of 2005](https://md.research.elamur.ai/free/en/papers/2005/)

## References

14 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. 2001. Hidden Markov Model for Hebrew part-of-speech tagging. Master’s thesis, Ben Gurion University, Israel. In Hebrew. [find](https://scholar.google.com/scholar?q=Meni+Adler%2E+2001%2E+Hidden+Markov+Model+for+Hebrew+part%2Dof%2Dspeech+tagging%2E+Master%E2%80%99s+thesis%2C+Ben+Gurion+University%2C+Israel%2E+In+Hebrew%2E)
2. Leonard Baum. 1972. An inequality and associated maximization technique in statistical estimation for probabilistic functions of a Markov process. In Inequalities III:Proceedings of the Third Symposium on Inequalities, University of California, Los Angeles, pp.1-8. [find](https://scholar.google.com/scholar?q=Leonard+Baum%2E+1972%2E+An+inequality+and+associated+maximization+technique+in+statistical+estimation+for+probabilistic+functions+of+a+Markov+process%2E+In+Inequalities+III%3AProceedings+of+the+Third+Symposiu)
3. Eric Brill. 1995. Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistic, 21:784–789. [find](https://scholar.google.com/scholar?q=Eric+Brill%2E+1995%2E+Transformation%2Dbased+error%2Ddriven+learning+and+natural+language+processing%3A+A+case+study+in+part%2Dof%2Dspeech+tagging%2E+Computational+Linguistic%2C+21%3A784%E2%80%93789%2E) [our card](https://md.research.elamur.ai/free/en/papers/brill-1995-transformation-based-error-driven-learning/)
4. David Carmel and Yoelle Maarek. 1999. Morphological disambiguation for Hebrew search systems. In Proceedings of the 4th international workshop,NGITS-99. [find](https://scholar.google.com/scholar?q=David+Carmel+and+Yoelle+Maarek%2E+1999%2E+Morphological+disambiguation+for+Hebrew+search+systems%2E+In+Proceedings+of+the+4th+international+workshop%2CNGITS%2D99%2E)
5. Eugene Charniak, Curtis Hendrickson, Neil Jacobson, and Mike Perkowitz. 1993. Equations for part-of-speech tagging. In National Conference on Artificial Intelligence, pages 784–789. [find](https://scholar.google.com/scholar?q=Eugene+Charniak%2C+Curtis+Hendrickson%2C+Neil+Jacobson%2C+and+Mike+Perkowitz%2E+1993%2E+Equations+for+part%2Dof%2Dspeech+tagging%2E+In+National+Conference+on+Artificial+Intelligence%2C+pages+784%E2%80%93789%2E)
6. K. W. Church. 1988. A stochastic parts program and noun phrase parser for unrestricted text. In Proc. of the Second Conference on Applied Natural Language Processing, pages 136–143, Austin, TX. [find](https://scholar.google.com/scholar?q=K%2E+W%2E+Church%2E+1988%2E+A+stochastic+parts+program+and+noun+phrase+parser+for+unrestricted+text%2E+In+Proc%2E+of+the+Second+Conference+on+Applied+Natural+Language+Processing%2C+pages+136%E2%80%93143%2C+Austin%2C+TX%2E)
7. Mona Diab, Kadri Hacioglu, and Daniel Jurafsky. 2004. Automatic tagging of Arabic text: From raw text to base phrase chunks. In HLT-NAACL 2004: Short Papers, pages 149–152. [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+HLT%2DNAACL+2004%3A+Short+Papers%2C+pages+149%E2%80%93152%2E)
8. S.M. Katz. 1987. Estimation of probabilities from sparse data from the language model component of a speech recognizer. IEEE Transactions of Acoustics, Speech and Signal Processing, 35(3):400–401. [find](https://scholar.google.com/scholar?q=S%2EM%2E+Katz%2E+1987%2E+Estimation+of+probabilities+from+sparse+data+from+the+language+model+component+of+a+speech+recognizer%2E+IEEE+Transactions+of+Acoustics%2C+Speech+and+Signal+Processing%2C+35%283%29%3A400%E2%80%93401%2E)
9. Young-Suk Lee, Kishore Papineni, Salim Roukos, Ossama Emam, and Hany Hassan. 2003. Language model based Arabic word segmentation. In ACL, pages 399–406. [find](https://scholar.google.com/scholar?q=Young%2DSuk+Lee%2C+Kishore+Papineni%2C+Salim+Roukos%2C+Ossama+Emam%2C+and+Hany+Hassan%2E+2003%2E+Language+model+based+Arabic+word+segmentation%2E+In+ACL%2C+pages+399%E2%80%93406%2E)
10. M. Levinger, U. Ornan, and A. Itai. 1995. Morphological disambiguation in Hebrew using a priori probabilities. Computational Linguistics, 21:383–404. [find](https://scholar.google.com/scholar?q=M%2E+Levinger%2C+U%2E+Ornan%2C+and+A%2E+Itai%2E+1995%2E+Morphological+disambiguation+in+Hebrew+using+a+priori+probabilities%2E+Computational+Linguistics%2C+21%3A383%E2%80%93404%2E)
11. Moshe Levinger. 1992. Morphological disambiguation in Hebrew. Master’s thesis, Computer Science Department, Technion, Haifa, Israel. In Hebrew. [find](https://scholar.google.com/scholar?q=Moshe+Levinger%2E+1992%2E+Morphological+disambiguation+in+Hebrew%2E+Master%E2%80%99s+thesis%2C+Computer+Science+Department%2C+Technion%2C+Haifa%2C+Israel%2E+In+Hebrew%2E)
12. Erel Segal. 2000. Hebrew morphological analyzer for Hebrew undotted texts. Master’s thesis, Computer Science Department, Technion, Haifa, Israel. [http://www.cs.technion.ac.il/-∼erelsgl/bxi/hmntx/teud.html](http://www.cs.technion.ac.il/-∼erelsgl/bxi/hmntx/teud.html). [↗](http://www.cs.technion.ac.il/-∼erelsgl/bxi/hmntx/teud.html)
13. K. Sima’an, A. Itai, Y. Winter, A. Altman, and N. Nativ. 2001. Building a tree-bank of Modern Hebrew text. Traitment Automatique des Langues, 42:347–380. [find](https://scholar.google.com/scholar?q=K%2E+Sima%E2%80%99an%2C+A%2E+Itai%2C+Y%2E+Winter%2C+A%2E+Altman%2C+and+N%2E+Nativ%2E+2001%2E+Building+a+tree%2Dbank+of+Modern+Hebrew+text%2E+Traitment+Automatique+des+Langues%2C+42%3A347%E2%80%93380%2E)
14. Andreas Stolcke. 2002. SRILM - an extensible language modeling toolkit. In ICSLP, pages 901–904, Denver, Colorado, September. [find](https://scholar.google.com/scholar?q=Andreas+Stolcke%2E+2002%2E+SRILM+%2D+an+extensible+language+modeling+toolkit%2E+In+ICSLP%2C+pages+901%E2%80%93904%2C+Denver%2C+Colorado%2C+September%2E)