  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.
