Optimizing Language Learning through Content-Driven Machine Learning

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Lexical experience is a fundamental organizer of the language processing system, where it is used in differing aspects of lexical cognition, from the organization of the mental lexicon to the acquisition of knowledge. Recent research within machine learning and computational cognitive science has led to the development of a class of models entitled distributional models of lexical semantics, which are designed to exploit statistical regularities within natural language to learn the meaning of words. These models have had remarkable success in accounting for language-based behaviours, from standard psycholinguistic experimental tasks to complex applied problems. However, there is one main failing of this approach: they do not take into account individual variability in learning history. Everyone has had different experiences with the world, and this leads to variability in language usage and comprehension. To overcome this problem, I developed a new machine learning framework, entitled experiential optimization, that can estimate the types of linguistic experience that an individual person might have had. The short-term goals of my research program is to gain a better understanding of the connection between lexical cognition and experience. In order to accomplish this goal, the proposed research uses a combination of theoretically-driven behavioural experimentation and large-scale computational modeling to further develop experiential optimization. Specifically, there are four points of knowledge that this grant proposes to discover: (a) a determination as to how past lexical experience impacts current lexical processing at a fine-grained level, (b) further develop the machinations of experiential optimization by using cutting edge methodology from machine learning, (c) generate a new, general test of reading experience, and (d) use the combined empirical and technological improvements to optimize an individual's learning potential. The results of these findings will lead to an improved empirical understanding of the connection between cognition and experience and also the development of corresponding cognitive technologies that can quantify this connection. The long-term goals of my research program is to use the cognitively-plausible machine learning and natural language processing models coming out of my basic research to develop automated educational technologies. The outcome of this grant will enable this through the continued development and refinement of new paradigms within cognitive computing and machine learning, while positioning Canada to continue to be a world leader in these areas. Students trained under this grant will receive state-of-the-art training in machine learning, cognitive modeling, data science, and psycholinguistics, a unique combination that will provide the trainees with valuable research experience that is desirable within both academia and industry.