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# Analogy in Contact: Modeling Maltese Plural Inflection

**Authors:** Sara Court; Andrea D. Sims; Micha Elsner · **Year:** 2023 · **Venue:** Proceedings of the Society for Computation in Linguistics 2023 (SCiL 2023), pp. 35–46 **Link:** [aclanthology.org/2023.scil-1.4](https://aclanthology.org/2023.scil-1.4/) · library `lingbulk-2023-court-analogy-contact-modeling-maltese-plural` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**2** citations](https://openalex.org/works?filter=cites:W6940143408) openalex-snapshot · 2026-10-14[Proceedings of the Society for Computation in Linguistics 2023](https://aclanthology.org/volumes/2023.scil-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2023.scil-1.4/)
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**Authors (3)**

1. [Sara Court](https://openalex.org/A5044955877) [ORCID](https://orcid.org/0009-0004-5510-8542) · [Semantic Scholar](https://www.semanticscholar.org/author/2029651568) · h 3 · 13 citations · 13 works
2. [Andrea D. Sims](https://openalex.org/A5001850880) [ORCID](https://orcid.org/0000-0002-0239-3050) · [Semantic Scholar](https://www.semanticscholar.org/author/2261357766) · h 10 · 1132 citations · 49 works
3. [Micha Elsner](https://openalex.org/A5017409643) [ORCID](https://orcid.org/0000-0002-1432-2129) · [Semantic Scholar](https://www.semanticscholar.org/author/1689233) · h 19 · 1611 citations · 82 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Maltese is a Semitic language with a large layer of Romance and English lexicon, and its nouns mix Semitic templates with concatenative plurals. The authors ask how much a noun's phonology and its etymology each predict its plural, and whether the templatic and affixal plurals form separate subsystems. They use 3,174 singular-plural pairs with etymology tags and 13 plural allomorph classes (classes with fewer than 20 nouns are dropped). A character-level LSTM classifier gives an upper bound on the conditional entropy of the plural class given the form, and the measure is normalised mutual information, always conditioned on gender. Phonology is more predictive than etymology in both models. For the allomorph classes, phonology shares 0.42 normalised bits with the plural class, etymology 0.22, and the three together 0.15; the concatenative-type model gives about half those values (0.21, 0.13, 0.06). The authors read this as evidence for analogy on phonological similarity at the word level, not for a split between templatic and affixal morphology.

## Ideas

- I1. Choose features by measured information: before writing a rule for a noun class, compute how much each candidate feature (final cluster, vowel count, a stem-final weak radical) tells about the class, conditioned on gender, and keep the features that add something beyond the others. This replaces intuition with a number on the lexicon in [The Noun, the Adjective and the Numeral](https://md.mova.elamur.ai/free/en/docs/languages/hbo/04-noun/).
- I2. Origin as its own feature: record the historical layer of a noun (native Semitic, Aramaic loan, Greek or Persian loan) as a separate tag, and test whether it adds information beyond the form, as the paper does with etymology. The measure is the tripartite term, the information shared by all three, which is what says whether the two features overlap. Target: [10. Historical layers: Biblical, Mishnaic, Medieval and Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/languages/he/10-historical-layers/).
- I3. Condition on gender in every measure: the paper follows Corbett and Fraser and computes all values given gender, so that gender-driven plural patterns are not counted as phonology. Mova's noun statistics in [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/) should do the same.
- I4. Use the bound, not only accuracy: with a held-out model, report the cross-entropy as an upper bound on the conditional entropy, averaged over 10 folds. The paper uses an LSTM for this; a simple count-based model can give the same kind of bound and is closer to Mova's rules.

## For Mova

I1 is a feature study for [The Noun, the Adjective and the Numeral](https://md.mova.elamur.ai/free/en/docs/languages/hbo/04-noun/) and [The noun in Biblical Aramaic](https://md.mova.elamur.ai/free/en/docs/languages/arc/nouns/). I2 and I3 go into [10. Historical layers: Biblical, Mishnaic, Medieval and Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/languages/he/10-historical-layers/) and [Nouns, adjectives, pronouns and numerals](https://md.mova.elamur.ai/free/en/docs/languages/he/04-nouns-adjectives-pronouns/). I4 is a measure for any noun-class rule set, run on held-out lexicon data with the fold split fixed. The LSTM itself is not for Mova; the measure is.

## Cite

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

```
@inproceedings{court2023analogy,
  title = {{Analogy in Contact: Modeling Maltese Plural Inflection}},
  author = {Court, Sara and Sims, Andrea D. and Elsner, Micha},
  booktitle = {Proceedings of the Society for Computation in Linguistics 2023},
  year = {2023},
  url = {https://aclanthology.org/2023.scil-1.4/}
}
```

- [← previous Morphotactic Modeling in an Open-source Multi-dialectal Arabic Morphological Analyzer and Generator](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2022-habash-morphotactic-modeling-open-source-multi/)
- [next → Feature Deletion by Head Movement: A New Solution to Agreement Asymmetries in Modern Standard Arabic](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2023-himmelreich-feature-deletion-head-movement-new/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

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