Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

Machine learning is a means to derive artificial intelligence by discovering patterns in
existing data. Here, we show that applying machine learning to ordinary human language …

Distributional semantics provides multidimensional, graded, empirically induced word
representations that successfully capture many aspects of meaning in natural languages, as …

In this work, we examine the extent to which embeddings may encode marginalized
populations differently, and how this may lead to a perpetuation of biases and worsened …

Big web data from sources including online news and Twitter are good resources for
investigating deep learning. However, collected news articles and tweets almost certainly …

Question Answering (QA) systems over Knowledge Graphs (KG) automatically answer
natural language questions using facts contained in a knowledge graph. Simple questions …

Clinical decision-making in healthcare is already being influenced by predictions or
recommendations made by data-driven machines. Numerous machine learning applications …

Over the last decade, AI models of language and word meaning have been dominated by
what we might call a statistics-of-occurrence, strategy: these models are deep neural net …