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

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. Semantic association is calculated in ten different implementations that vary the embedding model and context lengths. The effects of semantic association across the different implementations on the N400 and self-paced reading times are examined using Bayesian hierarchical models and Bayes factor. The results show that the choice of embedding model can alter the estimated effect of semantic association on both the N400 and self-paced reading times. Furthermore, the results demonstrate a promising potential of sentence embeddings for capturing semantic association, as only implementations relying on sentence embeddings indicate reliable results of semantic association beyond word predictability on both neural and behavioral measures. Together, these findings highlight the importance of methodological choices in quantifying semantic association.

Computation and Language (cs.CL)

arXiv:2606.07066 [cs.CL]

(or arXiv:2606.07066v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.07066 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.63317/49tvxys2q4zc Focus to learn more DOI(s) linking to related resources

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