[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/) / Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew # Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew **Authors:** [Roy Bar-Haim](https://md.mova.elamur.ai/free/en/docs/papers/authors/roy-bar-haim/); [Khalil Sima'an](https://md.mova.elamur.ai/free/en/docs/papers/authors/khalil-sima-an/); [Yoad Winter](https://md.mova.elamur.ai/free/en/docs/papers/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 [**10** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2005-haim-choosing-optimal-architecture-segmentation-pos/) 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) **Authors (3)** 1. Roy Bar-Haim 2. Khalil Sima'an 3. Yoad Winter Citations and author metrics from OpenAlex (); 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 [Prepositions, prefixes, conjunctions and particles](https://md.mova.elamur.ai/free/en/docs/languages/he/06-prepositions-particles/), with the spelling side in [2. Script and spelling](https://md.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 [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/). ## For Mova I1 goes into [Prepositions, prefixes, conjunctions and particles](https://md.mova.elamur.ai/free/en/docs/languages/he/06-prepositions-particles/) and [2. Script and spelling](https://md.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 [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/), to be measured. I3 is a test case for [11. Modern Hebrew in Universal Dependencies](https://md.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.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. ``` @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}, url = {https://aclanthology.org/W05-0706/} } ``` - [← previous A Comprehensive NLP System for Modern Standard Arabic and Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2002-kamir-comprehensive-nlp-system-modern-standard/) - [next → An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-adler-unsupervised-morpheme-based-hmm-hebrew/) - [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/) ## References 14 references, 0 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) 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. 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[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. 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