The action of reading, understanding and combining words to create meaningful phrases comes naturally to most people. Still, the processes that govern semantic composition in the human brain are not well understood. In this talk, I will explore semantics (word meaning) and semantic composition (combining the meaning of multiple words) using two data sources: a large text corpus, and brain recordings of people reading adjective noun phrases. Through the learning of latent representations, I will show that these two very different data sources are both consistent (they contain overlapping information), and are complementary (they contain non-overlapping, but still congruent information). These disparate data sources can be used together to further the study of semantics and semantic composition as grounded in the brain, or more abstractly as represented in patterns of word usage in corpora.
Alona Fyshe is an Assistant Professor in the Computer Science Department at the University of Victoria. Alona received her BSc and MSc in Computing Science from the University of Alberta, and a PhD in Machine Learning from Carnegie Mellon University. Alona uses machine learning to leverage large amounts of text and neuroimaging data to understand how people mentally combine words to create higher-order meaning. Alona works on the semantics of adjective-noun phrases, and is now exploring how composition impacts sentiment.