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This project explores how leveraging temporal relationships can significantly enhance the accuracy of concept detection. By quantifying these relationships as causality, we define a concept as a cause of another if they sequentially occur within a specific time frame. Our approach integrates causality into three distinct graph-based methodologies: an iterative approach, a dynamic programming method, and a Markov chain-inspired technique. Initial results indicate that applying at least one of these strategies leads to notable improvements in concept detection accuracy.
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Improving Concept Detection by Utilizing Temporal Relationships REU: Christian Weigandt Mentor: KhurramSoomro
Overview • We aim to take advantage of temporal relationships in order to improve the accuracy of concept detection • We quantify these temporal relationships as causality • We consider one concept to cause another if they occur subsequently within a specific timespan Improving Concept Detection by Utilizing Temporal Relationships – Christian Weigandt
Approaches • We incorporated causality into three different graph-based approaches: • An iterative approach • A dynamic programming approach • A Markov chain inspired approach • Using at least one of these methods, we are able to see improvements in concept detection Improving Concept Detection by Utilizing Temporal Relationships – Christian Weigandt
