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This paper explores the segmentation of tutorial dialogue into cohesive topics. A latent semantic space was created using conversations from human to human tutoring transcripts, allowing cohesion between utterances to be measured using vector similarity. Previous cohesion-based segmentation methods that focus on expository monologue are reapplied to these dialogues to create benchmarks for performance. A novel moving window technique using orthonormal bases of semantic vectors significantly outperforms these benchmarks on this dialogue segmentation task.

An orthonormal basis for topic segmentation in tutorial dialogue