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# Coming to Terms: Automatic Formation of Neologisms in Hebrew

**Authors:** Moran Mizrahi; Stav Yardeni Seelig; Dafna Shahaf · **Year:** 2020 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2020 **Link:** [aclanthology.org/2020.findings-emnlp.442](https://aclanthology.org/2020.findings-emnlp.442/) · library `findings-2020-mizrahi-coming-terms-automatic-formation-neologisms` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**3** citations](https://openalex.org/works?filter=cites:W3101488821) openalex · 2026-10-18[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/findings-2020-mizrahi-coming-terms-automatic-formation-neologisms/) of the 140673 papers whose bibliographies we read[Findings of the Association for Computational Linguistics: EMNLP 2020](https://aclanthology.org/volumes/2020.findings-emnlp/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.findings-emnlp.442/)
- [PDF](https://aclanthology.org/2020.findings-emnlp.442.pdf)
- [DOI 10.18653/v1/2020.findings-emnlp.442](https://doi.org/10.18653/v1/2020.findings-emnlp.442)
- [OpenAlex](https://openalex.org/W3101488821)

**Authors (3)**

1. Moran Mizrahi
2. [Stav Yardeni Seelig](https://openalex.org/A5055395417) h 1 · 3 citations · 1 works
3. [Dafna Shahaf](https://openalex.org/A5009660759) [ORCID](https://orcid.org/0000-0003-3261-0818) · h 22 · 1955 citations · 114 works

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

## What it is about

The system EBY proposes new Hebrew nouns for an English source word, following the Hebrew Academy's three methods: root-and-pattern, compounds and portmanteaus. Roots come from tf-idf over English definitions, translated into Hebrew and matched to roots in Hebrew Wiktionary and a dictionary; patterns come from WordNet sister terms. A character seq2seq model, trained on 3,365 root–pattern pairs from Wiktionary (0.68 accuracy), repairs the naive root-and-pattern combination, and a character 4-gram language model trained on 514K diacritized words ranks the candidates. In a user study with 177 participants on 20 source words, the algorithm wins 27 to 41% of pairwise comparisons against human suggestions, and it reaches the top quartile of the ranking for 10.3% of its outputs.

## Ideas

- I1. A rule table for special roots in Mova's Modern Hebrew generator: the paper says that about two-thirds of roots need a change when joined to a pattern, and names the cases (guttural letters, a root ending in aleph, gemination, diphthong to monophthong). Write them as finite rules in [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/) and test them on the same 3,365 pairs; the seq2seq's 0.68 is the baseline a rule system must beat.
- I2. Patterns as semantic classes with ambiguity kept: the paper notes that one pattern marks professions and also illnesses. A table that lists every category a pattern can take, instead of picking one, keeps the choice visible to the generator.
- I3. A deterministic candidate ranker: a character n-gram model with n = 4 and add-k smoothing (lowest perplexity of the n tried), scored per word length. Prefixes are stripped by a count rule (the stem is kept only if its count rises), with a stricter threshold for the definite article. A small BTreeMap-based version fits in Rust and can rank candidate forms for [2. Script and spelling](https://md.mova.elamur.ai/free/en/docs/languages/he/02-script-and-spelling/).

## For Mova

I1 goes into [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/), I2 into the same chapter as a category table, and I3 into a Rust tool under `tools/` for ranking candidate forms. The paper's binarized rating, with two cut-offs reported side by side, is a reporting rule that Mova can reuse for any human check.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.findings-emnlp.442](https://doi.org/10.18653/v1/2020.findings-emnlp.442)

```
@inproceedings{mizrahi2020coming,
  title = {{Coming to Terms: Automatic Formation of Neologisms in Hebrew}},
  author = {Mizrahi, Moran and Seelig, Stav Yardeni and Shahaf, Dafna},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
  year = {2020},
  doi = {10.18653/v1/2020.findings-emnlp.442},
  url = {https://doi.org/10.18653/v1/2020.findings-emnlp.442}
}
```

- [← previous A Truly Joint Neural Architecture for Segmentation and Parsing](https://md.mova.elamur.ai/free/en/docs/papers/eacl-2024-levi-truly-joint-neural-architecture-segmentation/)
- [next → HeGeL: A Novel Dataset for Geo-Location from Hebrew Text](https://md.mova.elamur.ai/free/en/docs/papers/findings-2023-argaman-hegel-novel-dataset-geo-location/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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