Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg  (5)
  • Computer science  (5)
  • Computer Science  (5)
Datasource
Material
Language
Years
Author, Corporation
Publisher
Keywords
Subjects(RVK)
  • 1
    Online Resource
    Online Resource
    Coarse-to-Fine Natural Language Processing (2012)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    ISBN: 9783642227431
    Language: English
    Pages: Online-Ressource (XXII, 105p. 33 illus., 13 illus. in color, digital)
    Series Statement: Theory and Applications of Natural Language Processing
    Series Statement: SpringerLink
    Series Statement: Bücher
    Parallel Title: Buchausg. u.d.T. Petrov, Slav Coarse-to-fine natural language processing
    RVK:
    ST 306
    Keywords: Computer Science ; Computer science ; Computer science ; Computational linguistics ; Statistical methods ; Natürliche Sprache ; Syntaktische Analyse ; Grammatik ; Latente Variable ; Maschinelles Lernen ; Automatische Spracherkennung ; Maschinelle Übersetzung ; Natürliche Sprache ; Syntaktische Analyse ; Grammatik ; Latente Variable ; Maschinelles Lernen ; Automatische Spracherkennung ; Maschinelle Übersetzung
    Abstract: 1.Introduction -- 2.Latent Variable Grammars for Natural Language Parsing -- 3.Discriminative Latent Variable Grammars -- 4.Structured Acoustic Models for Speech Recognition -- 5.Coarse-to-Fine Machine Translation Decoding -- 6.Conclusions and Future Work -- Bibliography
    Abstract: The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing. Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. This book is intended for students and researchers interested in statistical approaches to Natural Language Processing. Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach. Eugene Charniak (Brown University)
    Description / Table of Contents: Coarse-to-Fine Natural Language Processing; Foreword; Preface; Acknowledgements; Contents; List of Figures; List of Tables; Chapter 1 Introduction; 1.1 Coarse-to-Fine Models; 1.2 Coarse-to-Fine Inference; Chapter 2 Latent Variable Grammars for Natural Language Parsing; 2.1 Introduction; 2.1.1 Experimental Setup; 2.2 Manual Grammar Refinement; 2.2.1 Vertical and Horizontal Markovization; 2.2.2 Additional Linguistic Refinements; 2.3 Generative Latent Variable Grammars; 2.3.1 Hierarchical Estimation; 2.3.2 Adaptive Refinement; 2.3.3 Smoothing; 2.3.4 An Infinite Alternative; 2.4 Inference
    Description / Table of Contents: 2.4.1 Hierarchical Coarse-to-Fine Pruning2.4.1.1 Projections; 2.4.1.2 Estimating Projected Grammars; 2.4.1.3 Calculating Projected Expectations; 2.4.1.4 Hierarchical Projections; 2.4.1.5 Pruning Experiments; 2.4.2 Objective Functions for Parsing; 2.4.2.1 Minimum Bayes Risk Parsing; 2.4.2.2 Alternative Objective Functions; 2.5 Additional Experiments; 2.5.1 Experimental Setup; 2.5.2 Baseline Grammar Variation; 2.5.3 Final Results WSJ; 2.5.4 Multilingual Parsing; 2.5.5 Corpus Variation; 2.5.6 Training Size Variation; 2.6 Analysis; 2.6.1 Lexical Subcategories; 2.6.2 Phrasal Subcategories
    Description / Table of Contents: 2.6.3 Multilingual Analysis2.7 Summary and Future Work; Chapter 3 Discriminative Latent Variable Grammars; 3.1 Introduction; 3.2 Log-Linear Latent Variable Grammars; 3.3 Single-Scale Discriminative Grammars; 3.3.1 Efficient Discriminative Estimation; 3.3.1.1 Hierarchical Estimation; 3.3.1.2 Feature-Count Approximation; 3.3.2 Experiments; 3.3.2.1 Efficiency; 3.3.2.2 Regularization; 3.3.2.3 Final Test Set Results; 3.4 Multi-scale Discriminative Grammars; 3.4.1 Hierarchical Refinement; 3.4.2 Learning Sparse Multi-scale Grammars; 3.4.2.1 Hierarchical Training
    Description / Table of Contents: 3.4.2.2 Efficient Multi-scale Inference3.4.2.3 Feature Count Approximations; 3.4.3 Additional Features; 3.4.3.1 Unknown Word Features; 3.4.3.2 Span Features; 3.4.4 Experiments; 3.4.4.1 Sparsity; 3.4.4.2 Accuracy; 3.4.4.3 Efficiency; 3.4.4.4 Final Results; 3.4.5 Analysis; 3.5 Summary and Future Work; Chapter 4 Structured Acoustic Models for Speech Recognition; 4.1 Introduction; 4.2 Learning; 4.2.1 The Hand-Aligned Case; 4.2.2 Splitting; 4.2.3 Merging; 4.2.4 Smoothing; 4.2.5 The Automatically-Aligned Case; 4.3 Inference; 4.4 Experiments; 4.4.1 Phone Recognition; 4.4.2 Phone Classification
    Description / Table of Contents: 4.5 Analysis4.6 Summary and Future Work; Chapter 5 Coarse-to-Fine Machine Translation Decoding; 5.1 Introduction; 5.2 Coarse-to-Fine Decoding; 5.2.1 Related Work; 5.2.2 Language Model Projections; 5.2.3 Multipass Decoding; 5.3 Inversion Transduction Grammars; 5.4 Learning Coarse Languages; 5.4.1 Random Projections; 5.4.2 Frequency Clustering; 5.4.3 HMM Clustering; 5.4.4 JCluster; 5.4.5 Clustering Results; 5.5 Experiments; 5.5.1 Clustering; 5.5.2 Spacing; 5.5.3 Encoding Versus Order; 5.5.4 Final Results; 5.5.5 Search Error Analysis; 5.6 Summary and Future Work
    Description / Table of Contents: Chapter 6 Conclusions and Future Work
    Note: Description based upon print version of record
    DOI: 10.1007/978-3-642-22743-1
    URL: lizenzpflichtig
    URL: Volltext  (lizenzpflichtig)
    URL: Inhaltstext
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    BSZ
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Interactive Multi-modal Question-Answering (2011)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    ISBN: 9783642175251
    Language: English
    Pages: Online-Ressource (X, 294p. 60 illus, digital)
    Series Statement: Theory and Applications of Natural Language Processing
    Series Statement: SpringerLink
    Series Statement: Bücher
    Parallel Title: Buchausg. u.d.T. Interactive multi-modal question-answering
    RVK:
    ST 278
    Keywords: Information storage and retrieval systems ; Computer Science ; Computer science ; Multimedia systems ; Information storage and retrieva ; Computer science ; Information storage and retrieval systems ; Multimedia systems ; Aufsatzsammlung ; Frage-Antwort-System ; Multimodales System ; Mensch-Maschine-Kommunikation ; Natürlichsprachiges System ; Medizin ; Dialogsystem ; Information Extraction ; Textanalyse
    Abstract: Part I Introduction to the IMIX Programme -- Introduction. Antal van den Bosch and Gosse Bouma -- IMIX: Good Questions, Promising Answers. Eduard Hovy, Jon Oberlander, and Norbert Reithinger -- The IMIX demonstrator: an information search assistant for the medical domain. Dennis Hofs and Boris van Schooten and Rieks op den Akker -- Part II Interaction Management -- Vidiam: Corpus-based Development of a Dialogue Manager for Multimodal Question Answering. Boris van Schooten and Rieks op den Akker -- Multidimensional Dialogue Management. Simon Keizer, Harry Bunt, and Volha Petukhova -- Part III Fusing Text, Speech, and Images. Experiments in Multimodal Information Presentation. Charlotte van Hooijdonk, Wauter Bosma, Emiel Krahmer, Alfons Maes, and Mariët Theune -- Text-to-text generation for question answering. Wauter Bosma, Erwin Marsi, Emiel Krahmer and Mariët Theune -- Part IV Text Analysis for Question Answering Automatic Extraction of Medical Term Variants from Mutilingual Parallel Translations. Lonneke van der Plas, Jörg Tiedemann, and Ismail Fahmi -- Relation Extraction for Open and Closed Domain Question Answering . Gosse Bouma, Ismail Fahmi, and Jori Mur -- Constraint-Satisfaction Inference for Entity Recognition. Sander Canisius, Antal van den Bosch, and Walter Daelemans -- Extraction of Hypernymy Information from Text. Erik Tjong Kim Sang, Katja Hofmann and Maarten de Rijke.-Towards a Discourse-driven Taxonomic Inference Model . Piroska Lendvai
    Abstract: This book is the result of a group of researchers from different disciplines asking themselves one question: what does it take to develop a computer interface that listens, talks, and can answer questions in a domain? First, obviously, it takes specialized modules for speech recognition and synthesis, human interaction management (dialogue, input fusion, andmultimodal output fusion), basic question understanding, and answer finding. While all modules are researched as independent subfields, this book describes the development of state-of-the-art modules and their integration into a single, working application capable of answering medical (encyclopedic) questions such as "How long is a person with measles contagious?" or "How can I prevent RSI?". The contributions in this book, which grew out of the IMIX project funded by the Netherlands Organisation for Scientific Research, document the development of this system, but also address more general issues in natural language processing, such as the development of multidimensional dialogue systems, the acquisition of taxonomic knowledge from text, answer fusion, sequence processing for domain-specific entity recognition, and syntactic parsing for question answering. Together, they offer an overview of the most important findings and lessons learned in the scope of the IMIX project, making the book of interest to both academic and commercial developers of human-machine interaction systems in Dutch or any other language. Highlights include: integrating multi-modal input fusion in dialogue management (Van Schooten and Op den Akker), state-of-the-art approaches to the extraction of term variants (Van der Plas, Tiedemann, and Fahmi; Tjong Kim Sang, Hofmann, and De Rijke), and multi-modal answer fusion (two chapters by Van Hooijdonk, Bosma, Krahmer, Maes, Theune, and Marsi). Watch the IMIX movie at www.nwo.nl/imix-film . Like IBM's Watson, the IMIX system described in the book gives naturally phrased responses to naturally posed questions. Where Watson can only generate synthetic speech, the IMIX system also recognizes speech. On the other hand, Watson is able to win a television quiz, while the IMIX system is domain-specific, answering only to medical questions. "The Netherlands has always been one of the leaders in the general field of Human Language Technology, and IMIX is no exception. It was a very ambitious program, with a remarkably successful performance leading to interesting results. The teams covered a rema ...
    Description / Table of Contents: pt. 1. Introduction to the IMIX programme -- pt. 2. Interaction management -- pt. 3. Fusing text, speech, and images -- pt. 4. Text analysis for question answering -- pt. 5. Epilogue.
    Note: Includes bibliographical references
    DOI: 10.1007/978-3-642-17525-1
    URL: lizenzpflichtig
    URL: Volltext  (lizenzpflichtig)
    URL: Einführung/Vorwort
    URL: Inhaltsverzeichnis
    URL: Inhaltstext
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    BSZ
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Reinforcement Learning for Adaptive Dialogue Systems : A Data-driven Methodology for Dialogue Management and Natural Language Generation (2011)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    ISBN: 9783642249426
    Language: English
    Pages: Online-Ressource (XV, 253p. 50 illus., 27 illus. in color, digital)
    Series Statement: Theory and Applications of Natural Language Processing
    Series Statement: SpringerLink
    Series Statement: Bücher
    Parallel Title: Buchausg. u.d.T. Rieser, Verena Reinforcement learning for adaptive dialogue systems
    RVK:
    ST 306
    Keywords: Computer Science ; Computer science ; Artificial intelligence ; Translators (Computer programs) ; Computer science ; Artificial intelligence ; Translators (Computer programs) ; Mensch-Maschine-Kommunikation ; Dialogsystem ; Natürlichsprachiges System ; Multimodales System ; Lernendes System ; Bestärkendes Lernen ; Benutzerverhalten ; Simulation ; Automatische Sprachproduktion ; Mensch-Maschine-Kommunikation ; Dialogsystem ; Natürlichsprachiges System ; Multimodales System ; Lernendes System ; Bestärkendes Lernen ; Benutzerverhalten ; Simulation ; Automatische Sprachproduktion
    Abstract: 1.Introduction -- 2.Background -- 3.Reinforcement Learning for Information Seeking dialogue strategies -- 4.The bootstrapping approach to developing Reinforcement Learning-based strategies -- 5.Data Collection in aWizard-of-Oz experiment -- 6.Building a simulated learning environment from Wizard-of-Oz data -- 7.Comparing Reinforcement and Supervised Learning of dialogue policies with real users -- 8.Meta-evaluation -- 9.Adaptive Natural Language Generation -- 10.Conclusion -- References -- Example Dialogues -- A.1.Wizard-of-Oz Example Dialogues -- A.2.Example Dialogues from Simulated Interaction -- A.3.Example Dialogues from User Testing -- Learned State-Action Mappings -- Index
    Abstract: The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development - not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general
    Description / Table of Contents: Reinforcement Learning for Adaptive Dialogue Systems; Preface; Acknowledgements; Contents; Acronyms; Chapter 1 Introduction; 1.1 The Design Problem for Spoken Dialogue Systems; 1.2 Overview; 1.3 Structure of the Book; Chapter 2 (Background); Chapter 3 (Reinforcement Learning); Chapter 4 (Proof-of-Concept: Information Seeking Strategies); Chapter 5 (A Bootstrapping Approach to Develop Reinforcement Learning-based Strategies); Chapter 6 (Data Collection in aWizard-of-Oz Experiment); Chapter 7 (Building a Simulated Learning Environment from Wizard-of-Oz Data)
    Description / Table of Contents: Chapter 8 (Comparing Reinforcement and Supervised Learning of Dialogue Policies with Real Users)Chapter 9 (Natural Language Generation); Chapter 10 (Conclusion); Part I Fundamental Concepts; Chapter 2 Background; 2.1 Human-Computer Interaction; 2.2 Dialogue Strategy Development; 2.2.1 Conventional Development Lifecycle; 2.2.2 Evaluation and Strategy Quality Control; 2.2.2.1 Quality Control in Industry; 2.2.2.2 Evaluation Practises in Academia; 2.2.2.3 The PARADISE Evaluation Framework; 2.2.2.4 Strategy Re-Implementation; 2.2.3 Strategy Implementation
    Description / Table of Contents: 2.2.3.1 Implementation Practises in Industry2.2.3.2 Implementation Practises in Academia; 2.2.4 Challenges for Strategy Development; 2.3 Literature review: Learning Dialogue Strategies; 2.3.1 Machine Learning Paradigms; 2.3.2 Supervised Learning for Dialogue Strategies; 2.3.3 Dialogue as Decision Making under Uncertainty; 2.3.4 Reinforcement Learning for Dialogue Strategies; 2.4 Summary; Chapter 3 Reinforcement Learning; 3.1 The Nature of Dialogue Interaction; 3.1.1 Dialogue is Temporal; 3.1.2 Dialogue is Dynamic; 3.2 Reinforcement Learning-based Dialogue Strategy Learning
    Description / Table of Contents: 3.2.1 Dialogue as a Markov Decision Process3.2.1.1 Representing Dialogue as a Markov Decision Process; 3.2.1.2 Partially Observable Markov Decision Processes for Strategy Learning; 3.2.2 The Reinforcement Learning Problem; 3.2.2.1 Elements of Reinforcement Learning; 3.2.2.2 Algorithms for Reinforcement Learning; 3.2.2.3 The Curse of Dimensionality, and State Space Reduction; 3.2.3 Model-based vs. Simulation-based Strategy Learning; 3.2.3.1 Model-based Reinforcement Learning; 3.2.3.2 Simulation-based Reinforcement Learning; 3.3 Dialogue Simulation; 3.3.1 Wizard-of-Oz Studies
    Description / Table of Contents: 3.3.2 Computer-based Simulations3.3.3 Discussion; 3.4 Application Domains; 3.4.1 Information-Seeking Dialogue Systems; 3.4.2 Multimodal Output Planning and Information Presentation; 3.4.3 Multimodal Dialogue Systems for In-Car Digital Music Players; 3.5 Summary; Chapter 4 Proof-of-Concept: Information Seeking Strategies; 4.1 Introduction; 4.1.1 A Proof-of-Concept Study; 4.2 Simulated Learning Environments; 4.2.1 Problem Representation; 4.2.2 Database Retrieval Simulations; 4.2.2.1 Monotonic Database Simulation; 4.2.2.2 Random Database Simulation; 4.2.3 Noise Model; 4.2.4 User Simulations
    Description / Table of Contents: 4.2.5 Objective and Reward Function
    Note: Description based upon print version of record
    DOI: 10.1007/978-3-642-24942-6
    URL: lizenzpflichtig
    URL: Volltext  (lizenzpflichtig)
    URL: Inhaltsverzeichnis
    URL: Inhaltstext
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    BSZ
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    The Human Society and the Internet Internet-Related Socio-Economic Issues : First International Conference, Human.Society@Internet 2001 Seoul, Korea, July 46, 2001 Proceedings (2001)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    ISBN: 9783540477495
    Language: English
    Pages: Online-Ressource
    Series Statement: Lecture Notes in Computer Science 2105
    Series Statement: SpringerLink
    Series Statement: Bücher
    Series Statement: Lecture notes in computer science
    Parallel Title: Buchausg. u.d.T.: The human society and the internet
    DDC: 303.48/34
    RVK:
    SS 4800
    RVK:
    SR 850
    RVK:
    MS 1190
    Keywords: Computer Communication Networks ; Computer Science ; Computer science ; Education ; Information systems ; Management information systems ; Konferenzschrift 2001 ; Konferenzschrift 2001 ; Internet ; Electronic Commerce ; Bildung ; Mobile Telekommunikation ; Mensch-Maschine-Kommunikation
    URL: Volltext
    URL: Volltext
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    GBV
    KOBV
  • 5
    Online Resource
    Online Resource
    Digital Cities : Technologies, Experiences, and Future Perspectives (2000)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    ISBN: 9783540464228
    Language: English
    Pages: Online-Ressource
    Series Statement: Lecture Notes in Computer Science 1765
    Series Statement: SpringerLink
    Series Statement: Bücher
    Series Statement: Lecture notes in computer science
    Parallel Title: Buchausg. u.d.T.: Technologies, experiences, and future perspectives
    DDC: 004.67
    RVK:
    SS 4800
    Keywords: Artificial intelligence ; Computer Communication Networks ; Computer Science ; Computer science ; Information systems
    URL: Volltext
    URL: Volltext
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    GBV
    KOBV
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...