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Found 10 papers, 1 papers with code
Frequency effects in Linear Discriminative Learning
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 solution modelling the theoretical endstate of learning (EL) where all words are learned optimally.
Visual Grounding of Inter-lingual Word-Embeddings
We obtained visually grounded vector representations for these languages and studied whether visual grounding on one or multiple languages improved the performance of embeddings on word similarity and categorization benchmarks.
Making sense of spoken plurals
Our goal is to compare two models for the conceptualization of plurality.
How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning
This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision.
Semantic properties of English nominal pluralization: Insights from word embeddings
In comparison with our approach, a method from compositional distributional semantics, called FRACSS, predicted plural vectors that were more similar to the corpus-extracted plural vectors in terms of direction but not vector length.
Vector Space Morphology with Linear Discriminative Learning
This paper presents three case studies of modeling aspects of lexical processing with Linear Discriminative Learning (LDL), the computational engine of the Discriminative Lexicon model (Baayen et al., 2019).
Modeling morphology with Linear Discriminative Learning: considerations and design choices
This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning.
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training
The general approach is to embed both textual and visual information into a common space -the grounded space-confined by an explicit relationship between both modalities.
Learning Precise Spike Timings with Eligibility Traces
As a consequence, this limits in principle the inherent advantage of SNNs, that is, the potential to develop codes that rely on precise relative spike timings.
Understanding Idiomatic Variation
This study investigates the processing of idiomatic variants through an eye-tracking experiment.
