ArticleLinguistics vanguard : multimodal online journal2026
Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model.
Article in Linguistics vanguard : multimodal online journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model.Linguistics vanguard : multimodal online journal · 2026Article
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4 authors.
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Abstract
Recently, deep learning models have increasingly been used in cognitive modelling of language. This study asks whether deep learning can help us to better understand the learning problem that needs to be solved by speakers, above and beyond linear methods. We utilize the Discriminative Lexicon Model introduced by Baayen and colleagues, which models comprehension and production with mappings between numeric form and meaning vectors. While so far, these mappings have been linear (linear discriminative learning; LDL), in the present study we replace them with deep dense neural networks (deep discriminative learning; DDL). We find that DDL affords more accurate mappings for large and diverse datasets from English and Dutch, but not necessarily for Estonian and Taiwan Mandarin. DDL outperforms LDL in particular for words with pseudo-morphological structure such as
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