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# Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew

**Authors:** Moshe Levinger; [Uzzi Ornan](https://md.mova.elamur.ai/free/en/docs/papers/authors/uzzi-ornan/); [Alon Itai](https://md.mova.elamur.ai/free/en/docs/papers/authors/alon-itai/) · **Year:** 1995 · **Venue:** Computational Linguistics (ACL Anthology J95-3004) **Link:** [aclanthology.org/J95-3004](https://aclanthology.org/J95-3004/) · library `cl-1995-levinger-learning-morpho-lexical-probabilities-untagged` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**9** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/cl-1995-levinger-learning-morpho-lexical-probabilities-untagged/) of the 140673 papers whose bibliographies we read[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J95-3004/)
- [PDF](https://aclanthology.org/J95-3004.pdf)

**Authors (3)**

1. Moshe Levinger
2. Uzzi Ornan
3. Alon Itai

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

Unpointed Hebrew writing leaves most vowels out, so many word tokens have several morphological analyses; in a sample of press text about 55% of tokens were ambiguous. The authors' point is that the forms of one lexical entry help each other: some forms are unambiguous, so their analyses can be counted in an untagged corpus. For each ambiguous word they estimate the probability of each analysis, P(analysis | word), from how often that analysis is the right one, using the evidence of sibling forms of the same lexeme, with a morphological analyzer supplying the candidates and no hand tagging. They then use these probabilities to resolve ambiguity. On 21 test articles (3,400 tokens, about half of them ambiguous) they report results in the seventies to nineties, with separate figures for accuracy and applicability. Their error analysis finds 5 wrong out of 53 words in the test groups (9.5%), and a coverage failure, where the corpus lacks enough data, in only about 3% of ambiguous words.

## Ideas

- I1. Sibling-form voting for the unpointed mode of `tools/hbo`: when a token has several analyses, count how often each candidate lemma appears with unambiguous forms in the corpus, choose the analysis that its siblings support, and leave the token ambiguous otherwise. The lemma list can come from `macula dict`.
- I2. Report applicability and accuracy as two numbers, as the paper does: how many ambiguous tokens are fully resolved, and how many of those are right. Mova's `tools/hbo` and `tools/macula compare` reports should carry both.
- I3. A coverage fallback: when a lemma has no unambiguous sibling in the corpus, fall back to a cross-lexeme prior and log that the fallback was used. The paper finds this case rare, and the log would show how rare it is for Ancient Hebrew.

## For Mova

I1 is a rule for the unpointed mode of `tools/hbo` and for its segmenter tables. I2 is a measure for `tools/hbo` and `tools/macula compare`. I3 goes into [13. Biblical Hebrew in Mova: the tools](https://md.mova.elamur.ai/free/en/docs/languages/hbo/13-mova-tools/), with the sibling-form evidence noted in [The lexicon](https://md.mova.elamur.ai/free/en/docs/languages/hbo/10-lexicon/). The paper's Hebrew is Modern, so the numbers do not carry over; only the method does.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work.

```
@misc{levinger1995learning,
  title = {{Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew}},
  author = {Levinger, Moshe and Ornan, Uzzi and Itai, Alon},
  year = {1995},
  url = {https://aclanthology.org/J95-3004/}
}
```

- [← previous Modeling the Dalet Clitic in Historical Hebrew Texts: A New Prefix-Segmented BERT Model and Stylistic Analysis](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-tal-modeling-dalet-clitic-historical-hebrew/)
- [next → Identifying Semitic Roots: Machine Learning with Linguistic Constraints](https://md.mova.elamur.ai/free/en/docs/papers/cl-2008-daya-identifying-semitic-roots-machine-learning/)
- [↑ back to Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/)

## References

22 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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14. Jelinek, Frederick; Mercer, Robert L.; and Roukos, Salim (1992). "Principles of lexical language modeling for speech recognition." In Advances in Speech Signal Processing, edited by Sadaoki Furui and M. Mohan Sondhi, 651-699. Marcel Dekker, Inc. [find](https://scholar.google.com/scholar?q=Jelinek%2C+Frederick%3B+Mercer%2C+Robert+L%2E%3B+and+Roukos%2C+Salim+%281992%29%2E+%22Principles+of+lexical+language+modeling+for+speech+recognition%2E%22+In+Advances+in+Speech+Signal+Processing%2C+edited+by+Sadaoki+Furui+and+)
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