[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/aclbulk-2024-shmidman-otobert-identifying-suffixed-verbal-forms/) [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/) / OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature # OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature **Authors:** [Avi Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/avi-shmidman/), [Shaltiel Shmidman](https://md.mova.elamur.ai/free/en/docs/papers/authors/shaltiel-shmidman/) · **Year:** 2024 · **Venue:** Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024) **Link:** [aclanthology.org/2024.tsar-1.2](https://aclanthology.org/2024.tsar-1.2/) · library `aclbulk-2024-shmidman-otobert-identifying-suffixed-verbal-forms` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) [**0** citations](https://openalex.org/works?filter=cites:W4404780822) openalex · 2026-10-18[Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024)](https://aclanthology.org/volumes/2024.tsar-1/)[licence](https://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://aclanthology.org/2024.tsar-1.2/) - [PDF](https://aclanthology.org/2024.tsar-1.2.pdf) - [DOI 10.18653/v1/2024.tsar-1.2](https://doi.org/10.18653/v1/2024.tsar-1.2) - [OpenAlex](https://openalex.org/W4404780822) **Authors (2)** 1. [Avi Shmidman](https://openalex.org/A5013212409) h 5 · 122 citations · 53 works 2. [Shaltiel Shmidman](https://openalex.org/A5041361841) h 3 · 30 citations · 21 works Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart. ## What it is about Modern Hebrew literature sometimes fuses a verb with its direct-object pronoun into one word. The fused form is often identical to a common verb with another meaning, so readers are misled until the sentence ends. Annotators found 222 suffixed verbs in 135K words of literature (17.76 per 10K) against 7 in 185K words of news and 2 in 43K words of another news source; 64 of the literary cases were ambiguous. The authors pretrain a BERT-base model from scratch with a 128K vocabulary in which each verb-plus-pronoun pair is one token. A form is flagged when at least N of the top K masked-language-model predictions are such tokens. Tested on 2,589 unsuffixed and 264 suffixed homographs, K = 1000 gives precision 100% at N = 25 (recall 19%), 84% at N = 10 (recall 52%) and 54% at N = 5 (recall 74%). Fine-tuned classifiers on mBERT, AlephBERT and DictaBERT reach F1 0.39–0.58, and the DictaBERT morphological tagger has recall of 24%. The model and dataset are public; the model is Apache-licensed as stated in the paper. ## Ideas - I1. Paired readings in the lexicon: for each ambiguous form, store both analyses (a bare verb, and a verb with a person-gender object clitic) with the clitic's features. The paper's examples ("they ordered" against "he ordered it") make good seed pairs. In UD this is a verb with a pronoun object merged in the surface form, so the segmenter has to split it. - I2. Confidence tiers with a stated precision: the paper's thresholds give three tiers (precision 100%, 84%, 54%), each with a fixed hedge in the gloss ("likely", "perhaps"). Mova can output the same three tiers with the rule that fired, so the gloss says why. - I3. A held-out test: the 264 suffixed and 2,589 unsuffixed homographs are a clean test for the Modern Hebrew parser, and the paper's own tagger result (recall 24%) is a baseline to beat. Check the dataset's licence before use, because the text does not state it. ## For Mova I1 goes into [05b. Verb paradigms: the strong verb in seven binyanim and the weak roots](https://md.mova.elamur.ai/free/en/docs/languages/he/05b-verb-paradigms/) (paired readings) and [05. The verb: system, binyanim, tense and use](https://md.mova.elamur.ai/free/en/docs/languages/he/05-verbs/) (object clitics). I2 is an explanation rule in `prag`. I3 is a test for [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/). ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2024.tsar-1.2](https://doi.org/10.18653/v1/2024.tsar-1.2) ``` @inproceedings{shmidman2024otobert, title = {{OtoBERT: Identifying Suffixed Verbal Forms in Modern Hebrew Literature}}, author = {Shmidman, Avi and Shmidman, Shaltiel}, booktitle = {Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024)}, year = {2024}, doi = {10.18653/v1/2024.tsar-1.2}, url = {https://doi.org/10.18653/v1/2024.tsar-1.2} } ``` - [← previous From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2024-liebeskind-linguistics-practice-case-study-offensive/) - [next → Restoring Missing Spaces in Scraped Hebrew Social Media](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2025-shmidman-restoring-missing-spaces-scraped-hebrew/) - [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/) ## References 10 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. S. Y. Agnon. 1953a. At the Handles of the Lock. Schocken Publishing House. [find](https://scholar.google.com/scholar?q=S%2E+Y%2E+Agnon%2E+1953a%2E+At+the+Handles+of+the+Lock%2E+Schocken+Publishing+House%2E) 2. S. Y. Agnon. 1953b. Only Yesterday. Schocken Publishing House. [find](https://scholar.google.com/scholar?q=S%2E+Y%2E+Agnon%2E+1953b%2E+Only+Yesterday%2E+Schocken+Publishing+House%2E) 3. Tuqa Bani Yaseen, Qusai Ismail, Sarah Al-Omari, Eslam Al-Sobh, and Malak Abdullah. 2021. JUST-BLUE at SemEval-2021 task 1: Predicting lexical complexity using BERT and RoBERTa pre-trained language models. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 661–666, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Tuqa+Bani+Yaseen%2C+Qusai+Ismail%2C+Sarah+Al%2DOmari%2C+Eslam+Al%2DSobh%2C+and+Malak+Abdullah%2E+2021%2E+JUST%2DBLUE+at+SemEval%2D2021+task+1%3A+Predicting+lexical+complexity+using+BERT+and+RoBERTa+pre%2Dtrained+language+mod) 4. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019a. Bert: Pre-training of deep bidirectional transformers for language understanding. Preprint, arXiv:1810.04805. [↗](https://arxiv.org/abs/1810.04805) 5. Shaltiel Shmidman, Avi Shmidman, Moshe Koppel, and Reut Tsarfaty. 2024. MRL parsing without tears: The case of Hebrew. In Findings of the Association for Computational Linguistics ACL 2024, pages 4537–4550, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shaltiel+Shmidman%2C+Avi+Shmidman%2C+Moshe+Koppel%2C+and+Reut+Tsarfaty%2E+2024%2E+MRL+parsing+without+tears%3A+The+case+of+Hebrew%2E+In+Findings+of+the+Association+for+Computational+Linguistics+ACL+2024%2C+pages+4537) [our card](https://md.mova.elamur.ai/free/en/docs/papers/findings-2024-shmidman-mrl-parsing-without-tears-case/) 6. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019b. Bert: Pre-training of deep bidirectional transformers for language understanding. Preprint, arXiv:1810.04805. [↗](https://arxiv.org/abs/1810.04805) 7. Xinying Song, Alex Salcianu, Yang Song, Dave Dopson, and Denny Zhou. 2021. Fast wordpiece tokenization. Preprint, arXiv:2012.15524. [↗](https://arxiv.org/abs/2012.15524) 8. Eylon Gueta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker, and Reut Tsarfaty. 2023. Large pre-trained models with extra-large vocabularies: A contrastive analysis of hebrew bert models and a new one to outperform them all. Preprint, arXiv:2211.15199. [↗](https://arxiv.org/abs/2211.15199) [our card](https://md.mova.elamur.ai/free/en/docs/papers/hbo-2022-gueta-large-pre-trained-models-extra/) 9. Reut Tsarfaty and Yoav Goldberg. 2008. Word-based or morpheme-based? annotation strategies for Modern Hebrew clitics. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Reut+Tsarfaty+and+Yoav+Goldberg%2E+2008%2E+Word%2Dbased+or+morpheme%2Dbased%3F+annotation+strategies+for+Modern+Hebrew+clitics%2E+In+Proceedings+of+the+Sixth+International+Conference+on+Language+Resources+and+Eva) [our card](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2008-tsarfaty-word-based-or-morpheme-based/) 10. Abdelhak Kelious, Mathieu Constant, and Christophe Coeur. 2024. Complex word identification: A comparative study between ChatGPT and a dedicated model for this task. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 3645–3653, Torino, Italia. ELRA and ICCL. [find](https://scholar.google.com/scholar?q=Abdelhak+Kelious%2C+Mathieu+Constant%2C+and+Christophe+Coeur%2E+2024%2E+Complex+word+identification%3A+A+comparative+study+between+ChatGPT+and+a+dedicated+model+for+this+task%2E+In+Proceedings+of+the+2024+Joint+I)