LEADER 04525nam 22006495 450 001 9910983362203321 005 20251113210751.0 010 $a9783031743641 010 $a3031743644 024 7 $a10.1007/978-3-031-74364-1 035 $a(MiAaPQ)EBC31876426 035 $a(Au-PeEL)EBL31876426 035 $a(CKB)37193728800041 035 $a(OCoLC)1484359075 035 $a(DE-He213)978-3-031-74364-1 035 $a(EXLCZ)9937193728800041 100 $a20250111d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNatural Language Understanding in Conversational AI with Deep Learning /$fby Soyeon Caren Han, Henry Weld, Yan Li, Jean Lee, Josiah Poon 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (228 pages) 225 1 $aArtificial Intelligence (R0) Series 311 08$a9783031743634 311 08$a3031743636 327 $a1. Introduction to Natural Language Understanding -- 2. Prerequisites and Glossary for Natural Language Understanding -- 3. Single-turn Natural Language Understanding -- 4. Multi-turn Natural Language Understanding -- 5. Evaluating Natural Language Understanding -- 6. Applications and Case Studies in Natural Language Understanding -- 7. Challenges, Conclusion and Future Direction. 330 $aThis book provides a comprehensive introduction to conversational spoken language understanding and surveys recent advances in conversational AI. It guides the reader through the history, current advancements, and future of natural language understanding (NLU) in human-computer interactions. To this end, the book is structured in seven chapters: Introduction to Natural Language Understanding lays the foundation by tracing the evolution of NLU from early human communication to modern human-computer interactions. Prerequisites and Glossary for Natural Language Understanding then serves as a foundational resource, consolidating essential prerequisites and key terminologies relevant across the book. Single-Turn Natural Language Understanding looks at Single-Turn NLU, focusing on tasks that involve interpreting and processing user inputs in a single interaction, while Multi-Turn Natural Language Understanding moves on systems for extended interactions with users and explores techniques for managing dialogues, using context and integrating external knowledge bases. Next, Evaluating Natural Language Understanding discusses the annotation of datasets and various performance assessment methods, covering different levels of understanding from intent recognition to slot filling and domain classification. Applications and Case Studies in Natural Language Understanding subsequently shows real-world applications of NLU in finance, medicine, and law. Eventually Challenges, Conclusions and Future Directions explores the core obstacles hindering the advancement of NLU, including ambiguity, domain adaptation, data scarcity, and ethical concerns. By understanding these challenges, this chapter highlights the ongoing work needed to advance NLU. This book mainly targets researchers, PhD students, and professionals who are entering this field and look for a state-of-the-art introduction to NLU applied in conversational systems such as chatbots, large language models, or educational systems. 410 0$aArtificial Intelligence (R0) Series 606 $aNatural language processing (Computer science) 606 $aMachine learning 606 $aInformation storage and retrieval systems 606 $aNatural Language Processing (NLP) 606 $aMachine Learning 606 $aInformation Storage and Retrieval 615 0$aNatural language processing (Computer science) 615 0$aMachine learning. 615 0$aInformation storage and retrieval systems. 615 14$aNatural Language Processing (NLP). 615 24$aMachine Learning. 615 24$aInformation Storage and Retrieval. 676 $a006.35 700 $aHan$b Soyeon Caren$01786095 701 $aWeld$b Henry$01786096 701 $aLi$b Yan$0440717 701 $aLee$b Jean$01786097 701 $aPoon$b Josiah$01786098 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983362203321 996 $aNatural Language Understanding in Conversational AI with Deep Learning$94317501 997 $aUNINA