A theoretical model of dynamical grammatical gender shifting based on set-valued set function
Abstract
This study investigates the diverse characteristics of nouns, focusing on both semantic (e.g., countable/uncountable) and morphosyntactic (e.g., masculine/feminine) distinctions. We explore inter-word variations in gender markers in noun morphology. Grammatical gender shift is a widespread phenomenon in languages around the world. The aim is to uncover, through a data-driven formal model, the underlying patterns governing the variation of lexemes. To this end, we propose a new computational component dedicated to pairing items with morphological templates (e.g., the result of a generated item-template pair: (\textit{funas}, {N, +SG, −PL, −M, +F, −COL, +SING}), with its spell-out form: \textit{ða-funast} `cow'). This process is formally represented by the Template-Based and Modular Cognitive model. This proposed model, defined by a set-valued set function h: 𝒫(M) → 𝒫(M), predicts the non-linear dynamical mapping of lexical items onto morphological templates. By applying this formalism, we present a unified framework for understanding the complexities of morphological markings across languages. Through empirical observations, we demonstrate how these shifts, as well as non-gender shifts, arise during lexical changes, especially in Riffian. Our model posits that these variant markings emerge due to template shifts occurring during word and meaning formation. This study achieves two primary objectives. First, on the formal side, we prove the model's representational completeness in learning and prediction. Second, on the linguistic side, we challenge and broaden the conventional view of word formation by formally demonstrating that conversion is applicable to noun-to-noun derivation. This mathematical model not only contributes to a deeper understanding of morphosyntactic variation but also offers potential applications in other fields requiring precise modelling of linguistic patterns.
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BibTeX
@article{idrissi2026theoretical,
title = {{A theoretical model of dynamical grammatical gender shifting based on set-valued set function}},
author = {Mohamed El Idrissi},
journal = {Next Research},
year = {2026},
month = oct,
doi = {10.1016/j.nexres.2026.102529},
url = {https://hal.science/hal-05838519v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A theoretical model of dynamical grammatical gender shifting based on set-valued set function
AU - Mohamed El Idrissi
PY - 2026
DA - 2026/10/08
JO - Next Research
DO - 10.1016/j.nexres.2026.102529
UR - https://hal.science/hal-05838519v1
AB - This study investigates the diverse characteristics of nouns, focusing on both semantic (e.g., countable/uncountable) and morphosyntactic (e.g., masculine/feminine) distinctions. We explore inter-word variations in gender markers in noun morphology. Grammatical gender shift is a widespread phenomenon in languages around the world. The aim is to uncover, through a data-driven formal model, the underlying patterns governing the variation of lexemes. To this end, we propose a new computational component dedicated to pairing items with morphological templates (e.g., the result of a generated item-template pair: (\textit{funas}, {N, +SG, −PL, −M, +F, −COL, +SING}), with its spell-out form: \textit{ða-funast} `cow'). This process is formally represented by the Template-Based and Modular Cognitive model. This proposed model, defined by a set-valued set function h: 𝒫(M) → 𝒫(M), predicts the non-linear dynamical mapping of lexical items onto morphological templates. By applying this formalism, we present a unified framework for understanding the complexities of morphological markings across languages. Through empirical observations, we demonstrate how these shifts, as well as non-gender shifts, arise during lexical changes, especially in Riffian. Our model posits that these variant markings emerge due to template shifts occurring during word and meaning formation. This study achieves two primary objectives. First, on the formal side, we prove the model's representational completeness in learning and prediction. Second, on the linguistic side, we challenge and broaden the conventional view of word formation by formally demonstrating that conversion is applicable to noun-to-noun derivation. This mathematical model not only contributes to a deeper understanding of morphosyntactic variation but also offers potential applications in other fields requiring precise modelling of linguistic patterns.
SN - 3050-4759
ER -