A practical primer on processing semantic property norm data

Semantic property listing tasks require participants to generate short propositions (e.g., , ) for a specific concept (e.g., DOG). This task is the cornerstone of the creation of semantic property norms which are essential for modeling, stimuli creation, and understanding similarity between concepts. Despite the wide applicability of semantic property norms for a large variety of concepts across different groups of people, the methodological aspects of the property listing task have received less attention, even though the procedure and processing of the data can substantially affect the nature and quality of the measures derived from them. The goal of this paper is to provide a practical primer on how to collect and process semantic property norms. We will discuss the key methods to elicit semantic properties and compare different methods to derive meaningful representations from them. This will cover the role of instructions and test context, property preprocessing (e.g., lemmatization), property weighting, and relationship encoding using ontologies. With these choices in mind, we propose and demonstrate a processing pipeline that transparently documents these steps, resulting in improved comparability across different studies. The impact of these choices will be demonstrated using intrinsic (e.g., reliability, number of properties) and extrinsic measures (e.g., categorization, semantic similarity, lexical processing). This practical primer will offer potential solutions to several long-standing problems and allow researchers to develop new property listing norms overcoming the constraints of previous studies