Hierarchical Joint Learning for Natural Language Generation

Hierarchical Joint Learning for Natural Language Generation

Author: Nina Dethlefs

Publisher:

Published: 2013

Total Pages: 0

ISBN-13: 9781614993339

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Natural Language Generation NLG systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its situational circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. Traditionally, the generation process has been divided into distinct stages of decision making, e.g. content selection, utterance planning and surface realization. However, this sequential model does not account for the interdependencies that exist among these stages, which in practice can


Book Synopsis Hierarchical Joint Learning for Natural Language Generation by : Nina Dethlefs

Download or read book Hierarchical Joint Learning for Natural Language Generation written by Nina Dethlefs and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Language Generation NLG systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its situational circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. Traditionally, the generation process has been divided into distinct stages of decision making, e.g. content selection, utterance planning and surface realization. However, this sequential model does not account for the interdependencies that exist among these stages, which in practice can

Natural Language Generation in Interactive Systems

Natural Language Generation in Interactive Systems

Author: Amanda Stent

Publisher: Cambridge University Press

Published: 2014-06-12

Total Pages: 383

ISBN-13: 1139915916

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An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide serves to introduce graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on the research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software and other resources for pursuing research on natural language generation and interactive systems, including dialog systems, multimodal interfaces and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing and related fields.


Book Synopsis Natural Language Generation in Interactive Systems by : Amanda Stent

Download or read book Natural Language Generation in Interactive Systems written by Amanda Stent and published by Cambridge University Press. This book was released on 2014-06-12 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide serves to introduce graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on the research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software and other resources for pursuing research on natural language generation and interactive systems, including dialog systems, multimodal interfaces and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing and related fields.

Representation Learning for Natural Language Processing

Representation Learning for Natural Language Processing

Author: Zhiyuan Liu

Publisher: Springer Nature

Published: 2020-07-03

Total Pages: 319

ISBN-13: 9811555737

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This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.


Book Synopsis Representation Learning for Natural Language Processing by : Zhiyuan Liu

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Flexible Workflows

Flexible Workflows

Author: L. Grumbach

Publisher: IOS Press

Published: 2023-07-07

Total Pages: 340

ISBN-13: 1643683977

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Traditional workflow management systems support the fulfillment of business tasks by providing guidance along a predefined workflow model. Due to the shift from mass production to customization, flexibility has become important in recent decades, but the various approaches to workflow flexibility either require extensive knowledge acquisition and modeling, or active intervention during execution. Pursuing flexibility by deviation compensates for these disadvantages by allowing alternative paths of execution at run time without requiring adaptation to the workflow model. This work, Flexible Workflows: A Constraint- and Case-Based Approach, proposes a novel approach to flexibility by deviation, the aim being to provide support during the execution of a workflow by suggesting items based on predefined strategies or experiential knowledge, even in case of deviations. The concepts combine two familiar methods from the field of AI - constraint satisfaction problem solving, and process-oriented case-based reasoning. The combined model increases the capacity for flexibility. The experimental evaluation of the approach consisted of a simulation involving several types of participant in the domain of deficiency management in construction. The book contains 7 chapters covering foundations; domains and potentials; prerequisites; constraint based workflow engine; case based deviation management; prototype; and evaluation, together with an introduction, a conclusion and 3 appendices. Demonstrating high utility values and the promise of wide applicability in practice, as well as the potential for an investigation into the transfer of the approach to other domains, the book will be of interest to all those whose work involves workflow management systems.


Book Synopsis Flexible Workflows by : L. Grumbach

Download or read book Flexible Workflows written by L. Grumbach and published by IOS Press. This book was released on 2023-07-07 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional workflow management systems support the fulfillment of business tasks by providing guidance along a predefined workflow model. Due to the shift from mass production to customization, flexibility has become important in recent decades, but the various approaches to workflow flexibility either require extensive knowledge acquisition and modeling, or active intervention during execution. Pursuing flexibility by deviation compensates for these disadvantages by allowing alternative paths of execution at run time without requiring adaptation to the workflow model. This work, Flexible Workflows: A Constraint- and Case-Based Approach, proposes a novel approach to flexibility by deviation, the aim being to provide support during the execution of a workflow by suggesting items based on predefined strategies or experiential knowledge, even in case of deviations. The concepts combine two familiar methods from the field of AI - constraint satisfaction problem solving, and process-oriented case-based reasoning. The combined model increases the capacity for flexibility. The experimental evaluation of the approach consisted of a simulation involving several types of participant in the domain of deficiency management in construction. The book contains 7 chapters covering foundations; domains and potentials; prerequisites; constraint based workflow engine; case based deviation management; prototype; and evaluation, together with an introduction, a conclusion and 3 appendices. Demonstrating high utility values and the promise of wide applicability in practice, as well as the potential for an investigation into the transfer of the approach to other domains, the book will be of interest to all those whose work involves workflow management systems.

Neural Representations of Natural Language

Neural Representations of Natural Language

Author: Lyndon White

Publisher: Springer

Published: 2018-08-29

Total Pages: 122

ISBN-13: 9811300623

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This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.


Book Synopsis Neural Representations of Natural Language by : Lyndon White

Download or read book Neural Representations of Natural Language written by Lyndon White and published by Springer. This book was released on 2018-08-29 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.

Reinforcement Learning for Adaptive Dialogue Systems

Reinforcement Learning for Adaptive Dialogue Systems

Author: Verena Rieser

Publisher: Springer Science & Business Media

Published: 2011-11-23

Total Pages: 261

ISBN-13: 3642249426

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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.


Book Synopsis Reinforcement Learning for Adaptive Dialogue Systems by : Verena Rieser

Download or read book Reinforcement Learning for Adaptive Dialogue Systems written by Verena Rieser and published by Springer Science & Business Media. This book was released on 2011-11-23 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

From Narratology to Computational Story Composition and Back

From Narratology to Computational Story Composition and Back

Author: L. Berov

Publisher: IOS Press

Published: 2023-03-10

Total Pages: 362

ISBN-13: 1643683837

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Although both deal with narratives, the two disciplines of Narrative Theory (NT) and Computational Story Composition (CSC) rarely exchange insights and ideas or engage in collaborative research. The former has its roots in the humanities, and attempts to analyze literary texts to derive an understanding of the concept of narrative. The latter is in the domain of Artificial Intelligence, and investigates the autonomous composition of fictional narratives in a way that could be deemed creative. The two disciplines employ different research methodologies at contradistinct levels of abstraction, making simultaneous research difficult, while a close exchange between the two disciplines would undoubtedly be desirable, not least because of the complementary approach to their object of study. This book, From Narratology to Computational Story Composition and Back, describes an exploratory study in generative modeling, a research methodology proposed to address the methodological differences between the two disciplines and allow for simultaneous NT and CSC research. It demonstrates how implementing narratological theories as computational, generative models can lead to insights for NT, and how grounding computational representations of narrative in NT can help CSC systems to take over creative responsibilities. It is the interplay of these two strands that underscores the feasibility and utility of generative modeling. The book is divided into 6 chapters: an introduction, followed by chapters on plot, fictional characters, plot quality estimation, and computational creativity, wrapped up by a conclusion. The book will be of interest to all those working in the fields of narrative theory and computational creativity.


Book Synopsis From Narratology to Computational Story Composition and Back by : L. Berov

Download or read book From Narratology to Computational Story Composition and Back written by L. Berov and published by IOS Press. This book was released on 2023-03-10 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although both deal with narratives, the two disciplines of Narrative Theory (NT) and Computational Story Composition (CSC) rarely exchange insights and ideas or engage in collaborative research. The former has its roots in the humanities, and attempts to analyze literary texts to derive an understanding of the concept of narrative. The latter is in the domain of Artificial Intelligence, and investigates the autonomous composition of fictional narratives in a way that could be deemed creative. The two disciplines employ different research methodologies at contradistinct levels of abstraction, making simultaneous research difficult, while a close exchange between the two disciplines would undoubtedly be desirable, not least because of the complementary approach to their object of study. This book, From Narratology to Computational Story Composition and Back, describes an exploratory study in generative modeling, a research methodology proposed to address the methodological differences between the two disciplines and allow for simultaneous NT and CSC research. It demonstrates how implementing narratological theories as computational, generative models can lead to insights for NT, and how grounding computational representations of narrative in NT can help CSC systems to take over creative responsibilities. It is the interplay of these two strands that underscores the feasibility and utility of generative modeling. The book is divided into 6 chapters: an introduction, followed by chapters on plot, fictional characters, plot quality estimation, and computational creativity, wrapped up by a conclusion. The book will be of interest to all those working in the fields of narrative theory and computational creativity.

Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

Author: Stefan Wermter

Publisher: Springer Science & Business Media

Published: 1996-03-15

Total Pages: 490

ISBN-13: 9783540609254

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This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.


Book Synopsis Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing by : Stefan Wermter

Download or read book Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing written by Stefan Wermter and published by Springer Science & Business Media. This book was released on 1996-03-15 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.

Shallow Discourse Parsing for German

Shallow Discourse Parsing for German

Author: P. Bourgonje

Publisher: IOS Press

Published: 2021-07-13

Total Pages: 188

ISBN-13: 1643681931

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The last few decades have seen impressive improvements in several areas of Natural Language Processing. Nevertheless, getting a computer to make sense of the discourse of utterances in a text remains challenging. Several different theories which aim to describe and analyze the coherent structure of a well-written text exist, but with varying degrees of applicability and feasibility for practical use. This book is about shallow discourse parsing, following the paradigm of the Penn Discourse TreeBank, a corpus containing over 1 million words annotated for discourse relations. When it comes to discourse processing, any language other than English must be considered a low-resource language. This book relates to discourse parsing for German. The limited availability of annotated data for German means that the potential of modern, deep-learning-based methods relying on such data is also limited. This book explores to what extent machine-learning and more recent deep-learning-based methods can be combined with traditional, linguistic feature engineering to improve performance for the discourse parsing task. The end-to-end shallow discourse parser for German developed for the purpose of this book is open-source and available online. Work has also been carried out on several connective lexicons in different languages. Strategies are discussed for creating or further developing such lexicons for a given language, as are suggestions on how to further increase their usefulness for shallow discourse parsing. The book will be of interest to all whose work involves Natural Language Processing, particularly in languages other than English.


Book Synopsis Shallow Discourse Parsing for German by : P. Bourgonje

Download or read book Shallow Discourse Parsing for German written by P. Bourgonje and published by IOS Press. This book was released on 2021-07-13 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last few decades have seen impressive improvements in several areas of Natural Language Processing. Nevertheless, getting a computer to make sense of the discourse of utterances in a text remains challenging. Several different theories which aim to describe and analyze the coherent structure of a well-written text exist, but with varying degrees of applicability and feasibility for practical use. This book is about shallow discourse parsing, following the paradigm of the Penn Discourse TreeBank, a corpus containing over 1 million words annotated for discourse relations. When it comes to discourse processing, any language other than English must be considered a low-resource language. This book relates to discourse parsing for German. The limited availability of annotated data for German means that the potential of modern, deep-learning-based methods relying on such data is also limited. This book explores to what extent machine-learning and more recent deep-learning-based methods can be combined with traditional, linguistic feature engineering to improve performance for the discourse parsing task. The end-to-end shallow discourse parser for German developed for the purpose of this book is open-source and available online. Work has also been carried out on several connective lexicons in different languages. Strategies are discussed for creating or further developing such lexicons for a given language, as are suggestions on how to further increase their usefulness for shallow discourse parsing. The book will be of interest to all whose work involves Natural Language Processing, particularly in languages other than English.

Knowledge Representation and Inductive Reasoning Using Conditional Logic and Sets of Ranking Functions

Knowledge Representation and Inductive Reasoning Using Conditional Logic and Sets of Ranking Functions

Author: S. Kutsch

Publisher: IOS Press

Published: 2021-02-09

Total Pages: 186

ISBN-13: 164368163X

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A core problem in Artificial Intelligence is the modeling of human reasoning. Classic-logical approaches are too rigid for this task, as deductive inference yielding logically correct results is not appropriate in situations where conclusions must be drawn based on the incomplete or uncertain knowledge present in virtually all real world scenarios. Since there are no mathematically precise and generally accepted definitions for the notions of plausible or rational, the question of what a knowledge base consisting of uncertain rules entails has long been an issue in the area of knowledge representation and reasoning. Different nonmonotonic logics and various semantic frameworks and axiom systems have been developed to address this question. The main theme of this book, Knowledge Representation and Inductive Reasoning using Conditional Logic and Sets of Ranking Functions, is inductive reasoning from conditional knowledge bases. Using ordinal conditional functions as ranking models for conditional knowledge bases, the author studies inferences induced by individual ranking models as well as by sets of ranking models. He elaborates in detail the interrelationships among the resulting inference relations and shows their formal properties with respect to established inference axioms. Based on the introduction of a novel classification scheme for conditionals, he also addresses the question of how to realize and implement the entailment relations obtained. In this work, “Steven Kutsch convincingly presents his ideas, provides illustrating examples for them, rigorously defines the introduced concepts, formally proves all technical results, and fully implements every newly introduced inference method in an advanced Java library (...). He significantly advances the state of the art in this field.” – Prof. Dr. Christoph Beierle of the FernUniversität in Hagen


Book Synopsis Knowledge Representation and Inductive Reasoning Using Conditional Logic and Sets of Ranking Functions by : S. Kutsch

Download or read book Knowledge Representation and Inductive Reasoning Using Conditional Logic and Sets of Ranking Functions written by S. Kutsch and published by IOS Press. This book was released on 2021-02-09 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core problem in Artificial Intelligence is the modeling of human reasoning. Classic-logical approaches are too rigid for this task, as deductive inference yielding logically correct results is not appropriate in situations where conclusions must be drawn based on the incomplete or uncertain knowledge present in virtually all real world scenarios. Since there are no mathematically precise and generally accepted definitions for the notions of plausible or rational, the question of what a knowledge base consisting of uncertain rules entails has long been an issue in the area of knowledge representation and reasoning. Different nonmonotonic logics and various semantic frameworks and axiom systems have been developed to address this question. The main theme of this book, Knowledge Representation and Inductive Reasoning using Conditional Logic and Sets of Ranking Functions, is inductive reasoning from conditional knowledge bases. Using ordinal conditional functions as ranking models for conditional knowledge bases, the author studies inferences induced by individual ranking models as well as by sets of ranking models. He elaborates in detail the interrelationships among the resulting inference relations and shows their formal properties with respect to established inference axioms. Based on the introduction of a novel classification scheme for conditionals, he also addresses the question of how to realize and implement the entailment relations obtained. In this work, “Steven Kutsch convincingly presents his ideas, provides illustrating examples for them, rigorously defines the introduced concepts, formally proves all technical results, and fully implements every newly introduced inference method in an advanced Java library (...). He significantly advances the state of the art in this field.” – Prof. Dr. Christoph Beierle of the FernUniversität in Hagen