Takeshi Matsuda

Semantic Structure and Importance Extraction from Sequential Conversational Data via Dimensional Reduction

This study proposes a robust and semantically enriched framework for conversation understanding based on a composite distributed representation that incorporates both temporal adjacency and semantic proximity between utterances, enabling the visualization of key conversational connections.