Non-concatenative morphology poses a persistent challenge for NLP, yet structured quantitative resources for Amazigh (Berber) languages remain scarce. We present the first large-scale computational study of Tashlhiyt Berber plural formation, drawing on a richly annotated dataset of 1,185 noun paradigms with phonological, morphological and semantic features. We decompose the plural system into macro-level word-formation strategies and micro-level stem mutations, and evaluate predictability across ten target domains using linguistic feature models, N-gram baselines, and Bi-LSTM neural models. Results reveal a structured split: linguistic features decisively outperform neural models on systematic macro-level strategies (e.g., +44.5pp F1), while Bi-LSTMs better capture lexically idiosyncratic patterns. Rather than supporting a categorical rule/memory divide, this complementarity reveals gradient layers of regularity within a single morphological system. These findings demonstrate the value of linguistically informed annotation for probing morphological complexity in low-resource, typologically diverse languages. All data, code, and models are publicly available.

Grammatical gender assignment is governed by interacting phonological, semantic, and morphological factors whose relative weight varies across languages. We present the first large-scale computational analysis of gender in Hindi using a novel dataset of over 6,000 noun paradigms annotated for morphological, phonological, semantic, and lexical strata features. We tested inflection class against competing predictors with interpretable classifiers and neural networks and found that it dominates, predicting gender at ≈97% accuracy, while other features contribute negligibly. The dependence is asymmetric: inflection class nearly determines gender, whereas gender is only a partial predictor of inflection class. Inflection class is itself recoverable in part from phonological and etymological cues without reference to gender, which suggests a resolution to the bootstrapping problem: form-based cues scaffold inflection class, which in turn scaffolds gender. These findings establish Hindi as an extreme case of morphologically aligned gender and expose a limitation of language-agnostic approaches: methods that treat semantic and surface-form cues as broadly predictive of gender may miss Hindi’s near-deterministic dependence on inflection class. Our dataset enables probing whether multilingual models encode this dependency and whether it survives cross-lingual transfer.