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# Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic

**Authors:** [Nasser Zalmout](https://md.mova.elamur.ai/free/en/docs/papers/authors/nasser-zalmout/); [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/) · **Year:** 2017 · **Venue:** Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing **Link:** [aclanthology.org/D17-1073](https://aclanthology.org/D17-1073/) · library `aclbulk-2017-zalmout-don-t-throw-those-morphological` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**58** citations](https://openalex.org/works?filter=cites:W2757376562) openalex · 2026-10-18[**39** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2017-zalmout-don-t-throw-those-morphological/) of the 140673 papers whose bibliographies we read[Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D17-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/D17-1073/)
- [PDF](https://aclanthology.org/D17-1073.pdf)
- [DOI 10.18653/v1/d17-1073](https://doi.org/10.18653/v1/d17-1073)
- [OpenAlex](https://openalex.org/W2757376562)

**Authors (2)**

1. [Nasser Zalmout](https://openalex.org/A5089887413) h 16 · 803 citations · 43 works
2. [Nizar Habash](https://openalex.org/A5084517393) [ORCID](https://orcid.org/0000-0002-1831-3457) · h 57 · 12233 citations · 359 works

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

## What it is about

A morphological analyzer gives several readings for each Arabic word when taken out of context, and the task is to choose the right one in context. The authors train Bi-LSTM taggers for 14 non-lexical features (POS, case, number, gender, voice, mood, state, aspect, person, and the clitics) on Penn Arabic Treebank parts 1 to 3, with about 503,000 training words. The inputs are word embeddings plus affix features, with dictionary tags as an option. The lexical features (lemma and diacritized form) come from LSTM language models. Each analysis is scored by how many features it shares with the taggers, and the weights are tuned by Downhill Simplex on a held-out slice of training data. Full-analysis accuracy rises from 85.6% (MADAMIRA) to 90.0%, a 4.4-point gain; on out-of-vocabulary words it rises from 66.3% to 76.9%.

## Ideas

- I1. Rank the candidate analyses of a word by a weighted feature-match score: each candidate gets one point per feature that agrees with a per-feature tagger, with the weights tuned on held-out data. For Mova, the candidate splits of `tools/hbo` and the readings of `macula dict` can be ranked this way. The weights are a small table, so the choice stays deterministic and can be explained.
- I2. Keep the affix information as a rule. The paper's fixed three-character prefixes and suffixes give most of the gain without any lexicon. The Hebrew analogue is a table of proclitics (the conjunction ו, the prepositions ב, כ, ל, מ) read as features, not as a lookup.
- I3. Report the errors per feature, with the out-of-vocabulary split, as the paper does. Mark case and voice as the hard cases: they depend on syntax, so test them after `gparse` in `prag`, not inside the segmenter.

## For Mova

I1 and I2 go into `tools/hbo`: candidate ranking after the segmenter, with `macula dict` as the source of readings. I3 is the report format for the tests of `tools/macula`, and a syntax test in `prag`. The neural taggers are not reused; the ranking and the per-feature report are.

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 10.18653/v1/d17-1073](https://doi.org/10.18653/v1/d17-1073)

```
@inproceedings{zalmout2017don,
  title = {{Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic}},
  author = {Zalmout, Nasser and Habash, Nizar},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1073},
  url = {https://doi.org/10.18653/v1/d17-1073}
}
```

- [← previous A Neural Architecture for Dialectal Arabic Segmentation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-samih-neural-architecture-dialectal-arabic-segmentation/)
- [next → Derivational Relations in Arabic WordNet](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2018-batita-derivational-relations-arabic-wordnet/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

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