Predicting Declension Class from Form and Meaning
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Abstract (arXiv)
The noun lexica of many natural languages are divided into several declension classes with characteristic morphological properties. Class membership is far from deterministic, but the phonological form of a noun and/or its meaning can often provide imperfect clues. Here, we investigate the strength of those clues. More specifically, we operationalize this by measuring how much information, in bits, we can glean about declension class from knowing the form and/or meaning of nouns. We know that form and meaning are often also indicative of grammatical gender---which, as we quantitatively verify, can itself share information with declension class---so we also control for gender. We find for two Indo-European languages (Czech and German) that form and meaning respectively share significant amounts of information with class (and contribute additional information above and beyond gender). The three-way interaction between class, form, and meaning (given gender) is also significant. Our study is important for two reasons: First, we introduce a new method that provides additional quantitative support for a classic linguistic finding that form and meaning are relevant for the classification of nouns into declensions. Secondly, we show not only that individual declensions classes vary in the strength of their clues within a language, but also that these variations themselves vary across languages.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2021Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Levelcited by 34
- 2022A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processingcited by 13
- 2021On the Difficulty of Translating Free-Order Case-Marking Languagescited by 8
- 2024Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicalitycited by 3
- 2025Learning inflection classes using Adaptive Resonance Theorycited by 0
- 2025Learning inflection classes using Adaptive Resonance Theory17 Dec 2025
- 2024Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality6 Aug 2024
- 2022A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing9 May 2022
- 2021On the Difficulty of Translating Free-Order Case-Marking Languages13 Jul 2021
- 2021Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level13 May 2021