Semantic granularity in derivation

This paper investigates the degree of semantic precision in derivational processes, a crucial yet underexplored aspect of word-formation semantics that influences our understanding of compositionality, competition, and polysemy in derivation. The study focuses on the case of verb-to-noun derivation in French, using both manual analysis and computational techniques to determine the level of semantic granularity that best captures the semantic properties of derivational processes. A sample of 2,190 derived nouns formed with 30 deverbal processes is manually analyzed, distinguishing between ontological and relational semantic types, with three levels of granularity applied to each. The various combinations of semantic types and granularities are tested against computational representations of derivational processes generated from distributional semantic models. The results indicate that not only relational but also ontological types participate in the semantics of derivational processes, and that derivation operates at a fine level of semantic granularity. More broadly, the study illustrates how expert analysis of large datasets can be fruitfully associated with computational methods to address key research questions in theoretical linguistics. It also advocates for a close integration of lexical semantics and derivational morphology in the study of complex words.