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001476482 003__ OCoLC
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001476482 0247_ $$a10.1007/978-981-99-3885-8$$2doi
001476482 035__ $$aSP(OCoLC)1396062057
001476482 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dQGK$$dOCLCO
001476482 049__ $$aISEA
001476482 050_4 $$aQA76.9.H85
001476482 08204 $$a004.019$$223/eng/20230912
001476482 1001_ $$aXu, Hua.
001476482 24510 $$aIntent recognition for human-machine interactions /$$cHua Xu, Hanlei Zhang, Ting-En Lin.
001476482 260__ $$aSingapore :$$bSpringer,$$c2023.
001476482 300__ $$a1 online resource (162 p.).
001476482 336__ $$atext$$btxt$$2rdacontent
001476482 337__ $$acomputer$$bc$$2rdamedia
001476482 338__ $$aonline resource$$bcr$$2rdacarrier
001476482 4901_ $$aSpringerBriefs in Computer Science
001476482 500__ $$a5.4 Conclusion
001476482 504__ $$aReferences -- Part III: Unknown Intent Detection -- Chapter 5: Unknown Intent Detection Method Based on Model Post-Processing -- 5.1 Introduction -- 5.2 A Post-Processing for New Intent Detection -- 5.2.1 Classifiers -- BiLSTM -- CNN + CNN -- 5.2.2 SofterMax -- Temperature Scaling -- Probability Calibration -- Decision Boundary -- 5.2.3 Deep Novelty Detection -- 5.2.4 SMDN -- 5.3 Experiments -- 5.3.1 Datasets -- 5.3.2 Baselines -- 5.3.3 Experiment Settings -- Evaluation -- Hyper-Parameters -- 5.3.4 Experiment Results -- Single-Turn Dialogue Datasets -- Multi-Turn Dialogue Dataset
001476482 504__ $$aIncludes bibliographical references.
001476482 5050_ $$aIntro -- Preface -- Contents -- List of Figures -- List of Tables -- About the Authors -- Part I: Overview -- Chapter 1: Dialogue System -- 1.1 Review of Dialogue System -- References -- Chapter 2: Intent Recognition -- 2.1 Review of the Literature on Intent Representation -- 2.1.1 Discrete Representation -- One Hot Representation -- Bag of Word -- Term Frequency-Inverse Document Frequency (TF-IDF) -- N-gram -- 2.1.2 Distributed Representation -- Matrix-Based Distributed Representation -- Neural Network-Based Distributed Representation -- 2.1.3 Summary
001476482 5058_ $$a2.2 Review of Known Intent Classification -- 2.2.1 Review of Single-Model Intent Classification -- 2.2.2 Review of Bi-model Intent Classification -- 2.2.3 Summary -- 2.3 Review of Unknown Intent Detection -- 2.3.1 Unknown Intent Detection Based on Traditional Discriminant Model -- 2.3.2 Unknown Intent Detection Based on Open Set Recognition in Computer Vision -- 2.3.3 Unknown Intent Detection Based on Out-of-Domain Detection -- 2.3.4 Unknown Intent Detection Based on Other Methods -- 2.3.5 Summary -- 2.4 Review of New Intent Discovery -- 2.4.1 New Intent Discovery Based on Unsupervised Clustering
001476482 5058_ $$a2.4.2 New Intent Discovery Based on Semi-Supervised Clustering -- 2.4.3 Summary -- 2.5 Conclusion -- References -- Part II: Intent Classification -- Chapter 3: Intent Classification Based on Single Model -- 3.1 Introduction -- 3.2 Comparison Systems -- 3.2.1 Baseline Systems -- 3.2.2 NNLM-Based Utterance Classifier -- 3.2.3 RNN-Based Utterance Classifier -- 3.2.4 LSTM- and GRU-Based Utterance Classifier -- 3.3 Experiments -- 3.3.1 Datasets -- 3.3.2 Experiment Settings -- 3.3.3 Experiment Results -- 3.4 Conclusion -- References
001476482 5058_ $$aChapter 4: A Dual RNN Semantic Analysis Framework for Intent Classification and Slot -- 4.1 Introduction -- 4.2 Intent Classification and Slot Filling Task Methods -- 4.2.1 Deep Neural Network for Intent Detection -- 4.2.2 Recurrent Neural Network for Slot Filling -- 4.2.3 Joint Model for Two Tasks -- 4.3 Bi-Model RNN Structures for Joint Semantic Frame Parsing -- 4.3.1 Bi-model Structure with a Decoder -- 4.3.2 Bi-Model Structure without a Decoder -- 4.3.3 Asynchronous Training -- 4.4 Experiments -- 4.4.1 Datasets -- 4.4.2 Experiment Settings -- 4.4.3 Experiment Results -- 4.5 Conclusion
001476482 506__ $$aAccess limited to authorized users.
001476482 520__ $$aNatural 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.
001476482 588__ $$aDescription based on print version record.
001476482 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 12, 2023).
001476482 650_0 $$aHuman-computer interaction.
001476482 650_0 $$aUser interfaces (Computer systems)
001476482 650_0 $$aIntention (Logic)
001476482 650_6 $$aInterfaces utilisateurs (Informatique)
001476482 650_6 $$aIntention (Logique)
001476482 655_0 $$aElectronic books.
001476482 7001_ $$aZhang, Hanlei.
001476482 7001_ $$aLin, Ting-En.
001476482 77608 $$iPrint version:$$aXu, Hua$$tIntent Recognition for Human-Machine Interactions$$dSingapore : Springer,c2023$$z9789819938841
001476482 830_0 $$aSpringerBriefs in computer science.
001476482 852__ $$bebk
001476482 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3885-8$$zOnline Access$$91397441.1
001476482 909CO $$ooai:library.usi.edu:1476482$$pGLOBAL_SET
001476482 980__ $$aBIB
001476482 980__ $$aEBOOK
001476482 982__ $$aEbook
001476482 983__ $$aOnline
001476482 994__ $$a92$$bISE