Modeling semantic association in self-paced reading with language model embeddings

arXiv:2606.07066v1 Announce Type: new Abstract: Semantic association between a word and its context has been identified as an important component of reading comprehension, even when word predictability is accounted for. Recent research has highlighted the potential of language model ( LM) embeddings to quantify semantic association. Yet, embedding-based semantic association have been operationalized in a myriad of ways. In this study, we use embeddings from LMs to estimate semantic association on a corpus of joint electroencephalography (EEG) and self-paced reading of natural, Dutch texts. Sem

Source: arXiv cs.CL — read the full report at the original publisher.

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