Embedding derived animacy rankings offer insights into the sources of grammatical animacy (2025)

Abstract: In this study, we applied the semantic projection approach to animacy, a feature that has not been previously explored using this method. We compared the relative animacy rankings of nouns denoting animals, humans, objects, and first-, second-, and third-person pronouns, as derived from word embeddings, with rankings derived from human behavioral ratings of animacy and from grammatical patterns. Our results support the semantic projection approach as an effective method for deriving proxies of human perception from word embeddings and offer insights into the sources of grammatical animacy.

## introduction why are grammars the way they are? one perspective-advocated to varying degrees by proponents of usage-based linguistics, cognitive linguistics, emergentism, and other functionally oriented approaches @xcite -argues that grammatical structures emerge from exposure and interaction. according to this view, language learners develop grammars that align with existing linguistic norms because those are the structures they encounter. when learners' grammars diverge from established norms, these deviations are often attributed to limited or skewed linguistic input (e.g., in l1 and l2 learning, or contact-induced change). consequently, even when linguistic patterns conflict with cognitive or perceptual expectations, they are still learned. a compelling example is grammatical gender: any fluent speaker of german would classify the noun mädchen ('girl') as grammatically neuter, despite girls being biologically and conceptually feminine. studies on frequency effects and exemplar-based learning further support the role of distributional patterns in shaping both diachronic language change and real-time language processing. a contrasting perspective, most prominently associated with generative linguistics @xcite , posits that grammar is shaped by innate cognitive biases. according to this view, the acquisition of grammar is guided by inductive biases-cognitive predispositions that shape how learners interpret and generalize from linguistic data. in a naturalistic setting of language acquisition (as opposed to an unnatural/artificial setting where learners are exposed to inconsistent input, as in artificial language learning experiments), where learners' exposure generally aligns with the target grammar (e.g., in the case of grammatical gender in german), a functionalist explanation alone may seem sufficient. finding support for inductive biases would crucially involve identifying cases where linguistic input and grammatical structures diverge. such cases provide strong evidence that learners internalize grammars that do not simply mirror the distributional properties of their linguistic environment. in this study, we argue that grammatical animacy represents such a case. our contributions are summarized as follows: ## this study in this study, we apply the semantic projection method to investigate the animacy ranking as reflected in english word embeddings. specifically, we examine the divergent predictions made by the grammar-based hierarchy and the human ratingbased hierarchy, focusing on three relative rankings: the rankings 1) between animals and humans; 2) between human-denoting common nouns and pronouns; and 3) among pronouns. ## conclusion in this study, we applied the semantic projection approach to animacy, a feature that has not been previously explored using this method. we com-pared the relative animacy rankings of nouns denoting animals, humans, objects, and first-, second-, and third-person pronouns, as derived from word embeddings, with rankings derived from human behavioral ratings of animacy and from grammatical patterns. our results support the semantic projection approach as an effective method for deriving proxies of human perception from word embeddings and offer insights into the sources of grammatical animacy.