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# Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System

**Authors:** [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/); [Michael Elhadad](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-elhadad/) · **Year:** 2013 · **Venue:** Computational Linguistics **Link:** [aclanthology.org/J13-1007](https://aclanthology.org/J13-1007/) · library `cl-2013-goldberg-word-segmentation-unknown-word-resolution` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**26** citations](https://openalex.org/works?filter=cites:W2106657366) openalex · 2026-10-19[**13** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/cl-2013-goldberg-word-segmentation-unknown-word-resolution/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J13-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J13-1007/)
- [PDF](https://aclanthology.org/J13-1007.pdf)
- [DOI 10.1162/coli_a_00137](https://doi.org/10.1162/coli_a_00137)
- [OpenAlex](https://openalex.org/W2106657366)

**Authors (2)**

1. [Yoav Goldberg](https://openalex.org/A5028476919) [ORCID](https://orcid.org/0000-0002-6497-829X) · [Semantic Scholar](https://www.semanticscholar.org/author/2089067) · h 63 · 24540 citations · 401 works
2. [Michael Elhadad](https://openalex.org/A5013658527) [ORCID](https://orcid.org/0000-0002-5629-2351) · [Semantic Scholar](https://www.semanticscholar.org/author/1754680) · h 25 · 3695 citations · 137 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper builds a constituency parser for Modern Hebrew on the BerkeleyParser (PCFG with latent annotations) and extends it for a small treebank of 6,220 sentences. Three parts. (1) Word segmentation and parsing are done jointly over a lattice of possible segmentations, which beats a pipeline clearly: on the dev set, gold-segmentation parsing reaches F1 about 83.6 and the pipeline about 77.2 (5 split-merge cycles), so segmentation errors are costly. (2) A lexicon-based analyser (about 25,000 lemmas, extended by a learned guesser for unknown words) and an HMM tagger trained by EM on about 70M words are used to add coverage; the lexicon uses a lexical tag set that does not map simply to the treebank's syntactic tags. (3) Gender, number and person agreement are applied as a filter on the parser's output, which fixes a few agreement errors without new ones. The paper ends with the claim that the approach extends to other morphologically rich languages.

## Ideas

- I1. Keep all segmentations of an ambiguous token as lattice edges and let the parser choose, rather than fixing a segmentation first. In `tools/hbo` the segmenter output can carry alternatives, and `gparse` can pick the best path; measure it against the pipeline on the same dev set, as the paper does.
- I2. An agreement filter after parsing: reject an analysis if an adjective and its noun, or a verb and its subject, disagree in gender or number, and only then choose the next-best analysis. The filter sits outside the grammar, so it can be tested alone and reported as its own change.
- I3. Keep the lexical tag set and the syntactic tag set separate and write the bridge as a table with the cases that do not map (participles, modals). The paper says a simple one-to-one mapping is impossible, so the table must record each case.

## For Mova

I1 goes into `tools/hbo` (segmenter output as a lattice before `gparse`) and into [Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/) for the joint treatment. I2 goes into [Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/) for Biblical agreement and into [7. Syntax](https://md.mova.elamur.ai/free/en/docs/languages/he/07-syntax/) for Modern Hebrew. I3 goes into [11. Modern Hebrew in Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/languages/he/11-ud-analysis/) and [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/) for the participle case. The paper is NC: only this summary is kept, and nothing from it is built into a release.

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. [DOI 10.1162/coli_a_00137](https://doi.org/10.1162/coli_a_00137)

```
@article{goldberg2013word,
  title = {{Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System}},
  author = {Goldberg, Yoav and Elhadad, Michael},
  journal = {Computational Linguistics},
  year = {2013},
  doi = {10.1162/coli\_a\_00137},
  url = {https://doi.org/10.1162/coli_a_00137}
}
```

- [← previous TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/babylm-2025-gelboim-tafberta-learning-grammatical-rules-small/)
- [next → Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/coling-2018-amram-representations-architectures-neural-sentiment-analysis/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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