Paper: An Orthonormal Basis For Topic Segmentation In Tutorial Dialogue

ACL ID H05-1122
Title An Orthonormal Basis For Topic Segmentation In Tutorial Dialogue
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

This paper explores the segmentation of tutorial dialogue into cohesive topics. A latent semantic space was created using conversations from human to human tu- toring transcripts, allowing cohesion be- tween 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 se- mantic vectors significantly outperforms these benchmarks on this dialogue seg- mentation task.