Automated Team Discourse Annotation and Performance Prediction Using LSA
NEW MEXICO STATE UNIV LAS CRUCES
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We describe two approaches to analyzing and tagging team discourse using Latent Semantic Analysis LSA to predict team performance. The first approach automatically categorizes the contents of each statement made by each of the three team members using an established set of tags. Performance predicting the tags automatically was 15 below human agreement. These tagged statements are then used to predict team performance. The second approach measures the semantic content of the dialogue of the team as a whole and accurately predicts the teams performance on a simulated military mission.