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Frequency effects in Linear Discriminative Learning
March 19, 2024, 4:54 a.m. | Maria Heitmeier, Yu-Ying Chuang, Seth D. Axen, R. Harald Baayen
cs.CL updates on arXiv.org arxiv.org
Abstract: Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM; Baayen et al., 2018a, 2019) models lexical processing with linear mappings between words' forms and their meanings. So far, the mappings can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic …
abstract arxiv cs.cl effects forms linear processing recognition tasks type word words
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