1.

Record Nr.

UNISA996547972303316

Autore

Xu Hua (Writer on computer science)

Titolo

Intent recognition for human-machine interactions / / Hua Xu, Hanlei Zhang, Ting-En Lin

Pubbl/distr/stampa

Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023

ISBN

981-9938-85-6

Edizione

[1st ed. 2023.]

Descrizione fisica

1 online resource (162 pages)

Collana

SpringerBriefs in Computer Science, , 2191-5776

Altri autori (Persone)

ZhangHanlei

LinTing-En

Disciplina

004.019

Soggetti

User interfaces (Computer systems)

Human-computer interaction

Intention (Logic)

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Part I: Overview -- Chapter 1. Dialogue System -- Chapter 2. Intent Recognition -- Part II: Intent Classification -- Chapter 3. Intent Classification Based on Single Model -- Chapter 4. A Dual RNN Semantic Analysis Framework for Intent Classification and Slot -- Part III: Unknown Intent Detection -- Chapter 5. Unknown Intent Detection Method Based on Model Post-processing -- Chapter 6. Unknown Intent Detection Based on Large-Margin Cosine Loss -- Chapter 7. Unknown Intention Detection Method based on Dynamic Constraint Boundary -- Part IV: Discovery of Unknown Intents -- Chapter 8. Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement -- Chapter 9. Discovering New Intents with Deep Aligned Clustering -- Part V: Dialogue Intent Recognition Platform -- Chapter 10. Experiment Platform for Dialogue Intent Recognition based on Deep Learning -- Part VI: Summary and Future Work -- Chapter 11. Summary -- Appendix.

Sommario/riassunto

Natural interaction is one of the hottest research issues in human-computer interaction. At present, there is an urgent need for intelligent devices (service robots, virtual humans, etc.) to be able to understand intentions in an interactive dialogue. Focusing on human-computer understanding based on deep learning methods, the book



systematically introduces readers to intention recognition, unknown intention detection, and new intention discovery in human-computer dialogue. This book is the first to present interactive dialogue intention analysis in the context of natural interaction. In addition to helping readers master the key technologies and concepts of human-machine dialogue intention analysis and catch up on the latest advances, it includes valuable references for further research.