Modeling Meaning: A Kantian Intervention in Vector Space Semantics

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Arora, Nipun




This thesis discusses the implementation of a set of logical forms to enrich the way meaning is modeled in a vector-based system of conceptual memory. Vector-space models can account for a variety of psycho-linguistic phenomena by representing relationships between concepts as distance in a high-dimensional space. But they lack logical organizational structure without which inferential operations are impossible. Augmenting cognitive architectures with innate, logical structures might be the key to resolving this issue. But proposing such structures risks over-attributing the complexity of behavior to complexity in the architecture. I propose using Kant's critical work for a strong theory to select a minimal set of logical forms. The system is implemented onto vector space architecture in a model (Kantian-HDM) created in R programming language and has been published on GitHub. The results of the simulation run in the model are presented along with a description of inferential behavior exhibited.


Psychology - Cognitive
Artificial Intelligence




Carleton University

Thesis Degree Name: 

Master of Cognitive Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Cognitive Science

Parent Collection: 

Theses and Dissertations

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