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Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora
Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts.
Multiplex model of mental lexicon reveals explosive learning in humans
Our findings provide quantitative confirmation of existing conjectures about core structure in the mental lexicon and the importance of integrating multi-relational word-word interactions in psycholinguistic frameworks.
Multiplex lexical networks reveal patterns in early word acquisition in children
Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language.
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