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Improving Concept Detection by Utilizing Temporal Relationships

Improving Concept Detection by Utilizing Temporal Relationships. REU: Christian Weigandt. Mentor: Khurram Soomro. 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

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Improving Concept Detection by Utilizing Temporal Relationships

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  1. Improving Concept Detection by Utilizing Temporal Relationships REU: Christian Weigandt Mentor: KhurramSoomro

  2. 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

  3. 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

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