Cwic

Complex words in context

01.01.2024 bis 31.12.2026

Recent years have seen impressive advances in the fields of natural language processing (NLP) and artificial intelligence (AI). State-of-the-art language technologies have been made possible by advances in machine learning utilising many-layered 'deep' learning artificial neural networks. However, understanding what deep learning networks detect in language use, and what probabilistic information they exploit to generate predictions for computational language tasks, often remains unclear (but see Linzen & Baroni, 2021, for recent advances). For engineering purposes, this is not a problem, but for understanding language and the cognition of language processing, this state of affairs is highly unsatisfactory. The discriminative lexicon model (DLM) (Baayen, R. H. et al., 2019; Chuang & Baayen, R. H., 2021) is an attempt to combine the strengths of the mathematics of error-driven learning with the new possibilities offered by word embeddings for the computational modeling of the mental lexicon and lexical processing. Word embeddings, which we will also refer to as 'semantic vectors', represent word meanings as points in a high-dimensional space calculated from word usage in large text corpora.