Unlocking the complexity of phrasal composition: An interplay between semantic features and linguistic relations

Understanding the computational operations involved in conceptual composition is fundamental for theories of language. However, the existing literature on this topic remains fragmented, comprising disconnected theories from various fields. For instance, while formal semantic theories in Linguistics rely on type-driven interpretation without explicitly representing the conceptual content of lexical items, neurolinguistic research suggests that the brain is sensitive to conceptual factors during word composition. What is the relationship between these two types of theories? Do they describe two distinct aspects of composition, operating independently, or do they connect in some way during interpretation by our brain? To probe this, we explored how the mathematical operations explaining the combination of two words into a phrase are affected by the semantic content of items and the formal linguistic relations between the combining items. For six phrase types that varied properties relevant to type-driven interpretation such as modification vs. argument-saturation and modifier context sensitivity, we collected human ratings of experiential semantic features both for the phrases and for all the individual words within the phrases. We then compared the ability of different computational combination rules to explain the phrase ratings based on the individual word ratings. Our results indicate that composition operations are not one-size-fits-all but rather depend on both feature type and linguistic relation. For example, in intersective Adjective-Noun phrases, addition is used to merge attention-related features, while color features are predominantly determined by the first word's ratings. In the case of social features, the verb chiefly guides interpretation in Verb-Noun phrases, whereas in Noun-Noun phrases, the model employs multiplication to combine the social features of the nouns.