Meaning creation in novel noun-noun compounds: humans and language models

Abstract

The interpretation of novel noun-noun compounds (NNCs, e.g. “devil salary”) requires the combination of nouns in the absence of syntactic cues, an interesting facet of complex meaning creation. Here we examine unconstrained interpretations of a large set of novel NNCs, to investigate how NNC constituents are combined into novel complex meanings. The data show that words’ lexical-semantic features (e.g. material, agentivity, imageability, semantic similarity) differentially contribute to the grammatical relations and the semantics of NNC interpretations. Further, we demonstrate that passive interpretations incur higher processing cost (longer interpretation times and more eye-movements) than active interpretations. Finally, we show that large language models (GPT-2, BERT, RoBERTa) can predict whether a NNC is interpretable by human participants and estimate differences in processing cost, but do not exhibit sensitivity to more subtle grammatical differences. The experiments illuminate how humans can use lexical-semantic features to interpret NNCs in the absence of explicit syntactic information.