This paper introduces a novel annotation framework for the fine-grained modeling of Noun Phrases’ (NPs) genericity in natural language. The framework is designed to be simple and intuitive, making it accessible to non-expert annotators and suitable for crowd-sourced tasks. Drawing from theoretical and cognitive literature on genericity, this framework is grounded in established linguistic theory. Through a pilot study, we created a small but crucial annotated dataset of 324 sentences, serving as a foundation for future research. To validate our approach, we conducted an evaluation comparing our continuous annotations with existing binary annotations on the same dataset, demonstrating the framework’s effectiveness in capturing nuanced aspects of genericity. Our work offers a practical resource for linguists, providing a first annotated dataset and an annotation scheme designed to build real-language datasets that can be used in studies on the semantics of genericity, and NLP practitioners, contributing to the development of commonsense knowledge repositories valuable in enhancing various NLP applications.

Research on conceptual abstraction has investigated the differences in contextual distributions, or “contextual variability,” of abstract and concrete concept words (e.g., *love* vs. *cat*). Empirical studies on this topic show that abstract words tend to occur in diverse linguistic contexts, while concrete words are typically constrained within more homogeneous contexts. Nonetheless, these investigations have somewhat overlooked a factor that influences both abstract and concrete concepts: *Categorial Specificity*, which denotes the inclusiveness of a category (e.g., *ragdoll* vs. *mammal*). We argue that more specific words are tied to narrower domains, independently or whether they are concrete or abstract, thus resulting in a diminished degree of contextual variability when compared to generic terms. In this study, we used distributional models to investigate the interplay between contextual variability, concreteness, specificity, and their interaction. Analyzing 676 English nouns, we found that contextual variability is explained by both concreteness and specificity: more specific words have closer contexts, while generic words, whether abstract or concrete, exhibit less related contexts.

A large amount of literature on conceptual abstraction has investigated the differences in contextual distribution (namely “contextual variability”) between abstract and concrete concept words (“joy” vs. “apple”), showing that abstract words tend to be used in a wide variety of linguistic contexts. In contrast, concrete words usually occur in a few very similar contexts. However, these studies do not take into account another process that affects both abstract and concrete concepts alike: “specificity, that is, how inclusive a category is (“ragdoll” vs. “mammal”). We argue that the more a word is specific, the more its usage is tied to specific domains, and therefore its contextual variability is more limited compared to generic words. In this work, we used distributional semantic models to model the interplay between contextual variability measures and i) concreteness, ii) specificity, and iii) the interaction between the two variables. Distributional analyses on 662 Italian nouns showed that contextual variability is mainly explainable in terms of specificity or by the interaction between concreteness and specificity. In particular, the more specific a word is, the more its contexts will be close to it. In contrast, generic words have less related contexts, regardless of whether they are concrete or abstract.