Natural language and machine translation Books

Book SynopsisThis book is a first-stop introduction to corpus-based language research. It takes the reader systematically through the practical problems and benefits including the points to be reviewed before using computers, obtaining corpus material, the main analytical tools and the most important applications of computerised natural language processing. Each chapter offers guidance on programming where appropriate at a level suitable for readers with no prior experience, and provides exercises to help the reader to apply the principles covered. Case studies are used to show how the techniques are used in genuine research situations.Trade ReviewWell illustrated ...The book contains much good practical advice for students. -- Chris Butler, University College of Ripon and York St John A useful and very accessible introduction to the use of nonlinguistic computational techniques in corpus analysis. -- Frank Van Eynde Well illustrated ...The book contains much good practical advice for students. A useful and very accessible introduction to the use of nonlinguistic computational techniques in corpus analysis.Table of ContentsWhy use a computer?; first capture your data; examining the catch - the use of frequency lists; studying the environment - using concordances; the sociology of words - collocation analysis; putting them in their place - tagging, parsing and so on; the leading edge - applications of Natural Language Processing; case studies. Appendices: Programming languages for language programming; Awk - a very brief introduction; detailed programming examples.

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Book SynopsisVoice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps. about the technologyNatural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines. about the book Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what's inside Design, develop, and deploy basic NLP applications NLP libraries such as AllenNLP and Fairseq Advanced NLP concepts such as attention and transfer learning about the readerAimed at intermediate Python programmers. No mathematical or machine learning knowledge required. about the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.

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Book SynopsisThe big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features · Selecting the right AI project to meet specific business goals · Economizing resources to deliver the best value for money · How to measure the success of your AI efforts in the business terms · Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

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Book SynopsisOptimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner. Automated Machine Learning in Action, filled with hands-onexamples and written in an accessible style, reveals how premade machine learning components can automate time-consuming ML tasks. Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input. You'll quickly run through machine learning basics thatopen upon AutoML to non-data scientists, before putting AutoML into practicefor image classification, supervised learning, and more. Automated machine learning (AutoML) automates complex andtime-consuming stages in a machine learning pipeline with pre packaged optimal solutions. This frees up data scientists from data processing and manualtuning, and lets domain experts easily apply machine learning models to their projects.Trade Review“Automating automation itself is a new concept and this book does justice to it in terms of explaining the concepts, sharing real world advancements, use cases and research related to the topic. “ Satej KumarSahu “A book with a lot of promise, covering a topic that's like to become hot in the next year or so. Read this now, and get ahead of the curve!” RichardVaughan “A nice introduction to AutoML, its ambitions, and challenges bothin theory and in practice.” Alain Couniot “Helps you to clearly understand the process of Machine Learning automation. The examples are clear, concise, and applicable to the real world.”Walter Alexander Mata López “The author's friendly style makes novices feel ready to try outAutoML tools.” Gaurav Kumar Leekha “A great book to take your machine learning skills to the next level.” Harsh Raval “An impressive effort by the authors to break down a complex MLtopic into understandable chunks.” Venkatesh Rajagopal

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Book SynopsisThis book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.Table of ContentsPreface.- Acknowledgments.- Glossary.- Introducing Dialogue Systems.- Rule-Based Dialogue Systems: Architecture, Methods, and Tools.- Statistical Data-Driven Dialogue Systems.- Evaluating Dialogue Systems.- End-to-End Neural Dialogue Systems.- Challenges and Future Directions.- Bibliography.- Author's Biography .

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Book SynopsisThis book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments. The authors present the latest developments and techniques for building such continual learning dialogue systems. The book explains how these developments allow systems to continuously learn new language expressions, lexical and factual knowledge, and conversational skills through interactions and dialogues. Additionally, the book covers techniques to acquire new training examples for learning new tasks during the conversation. The book also reviews existing work on lifelong learning and discusses areas for future research. Table of Contents1 Introduction.- 2 Open-world Continual Learning: A Framework.- 3 Continuous Factual Knowledge Learning in Dialogues.- 4 Continuous and Interactive Language Learning and Grounding.- 5 Continual Learning in Chit-chat Systems.- 6 Continual Learning for Task-oriented Dialogue Systems.- 7 Continual Learning of Conversational Skills.- 8 Conclusion and Future Directions.

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Book SynopsisThis white paper is part of a series that promotes knowledge about language technology and its potential. It addresses educators, journalists, politicians, language communities and others. The availability and use of language technology in Europe varies between languages. Consequently, the actions that are required to further support research and development of language technologies also differ for each language. The required actions depend on many factors, such as the complexity of a given language and the size of its community. META-NET, a Network of Excellence funded by the European Commission, has conducted an analysis of current language resources and technologies. This analysis focused on the 23 official European languages as well as other important national and regional languages in Europe. The results of this analysis suggest that there are many significant research gaps for each language. A more detailed expert analysis and assessment of the current situation will help maximise the impact of additional research and minimize any risks. META-NET consists of 54 research centres from 33 countries that are working with stakeholders from commercial businesses, government agencies, industry, research organisations, software companies, technology providers and European universities. Together, they are creating a common technology vision while developing a strategic research agenda that shows how language technology applications can address any research gaps by 2020.

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Book SynopsisHow does a parser, a device that imposes an analysis on a string of symbols so that they can be interpreted, work? More specifically, how does the parser in the human cognitive mechanism operate? Using a wide range of empirical data concerning human natural language processing, Bradley Pritchett demonstrates that parsing performance depends on grammatical competence, not, as many have thought, on perception, computation, or semantics. Pritchett critiques the major performance-based parsing models to argue that the principles of grammar drive the parser; the parser, furthermore, is the apparatus that tries to enforce the conditions of the grammar at every point in the processing of a sentence. In comparing garden path phenomena, those instances when the parser fails on the first reading of a sentence and must reanalyze it, with occasions when the parser successfully functions the first time around, Pritchett makes a convincing case for a grammar-derived parsing theory.

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Book SynopsisExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.

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Book SynopsisThe Handbook provides a comprehensive overview of the concepts, methodologies, and applications being undertaken today in computational linguistics and natural language processing.Trade Review“The overall evaluation is therefore definitely very good: the work is solid, complete and definitely an important reference for NLP and CL.” (Linguistlist, 14 January 2014) “Altogether, this Handbookcovers a wide variety of topics in NLP and CL and, is of particular use to researchers in the field of MT. On a more general note, graduate students or novice researchers can utilise this book as a comprehensive starting point for their area of interest within NLP or CL … All in all, this is very well compiled book, which effectively balances the width and depth of theories and applications in two very diverse yet closely related fields of language research.” (Machine Translation, 18 March 2012)Table of ContentsList of Figures ix List of Tables xiv Notes on Contributors xv Preface xxiii Introduction 1 Part I Formal Foundations 9 1 Formal Language Theory 11 Shuly Wintner 2 Computational Complexity in Natural Language 43 Ian Pratt-Hartmann 3 Statistical Language Modeling 74 Ciprian Chelba 4 Theory of Parsing 105 Mark-Jan Nederhof And Giorgio Satta Part II Current Methods 131 5 Maximum Entropy Models 133 Robert Malouf 6 Memory-Based Learning 154 Walter Daelemans And Antal Van Den Bosch 7 Decision Trees 180 Helmut Schmid 8 Unsupervised Learning and Grammar Induction 197 Alexander Clark And Shalom Lappin 9 Artificial Neural Networks 221 James B. Henderson 10 Linguistic Annotation 238 Martha Palmer And Nianwen Xue 11 Evaluation of NLP Systems 271 Philip Resnik And Jimmy Lin Part III Domains of Application 297 12 Speech Recognition 299 Steve Renals And Thomas Hain 13 Statistical Parsing 333 Stephen Clark 14 Segmentation and Morphology 364 John A. Goldsmith 15 Computational Semantics 394 Chris Fox 16 Computational Models of Dialogue 429 Jonathan Ginzburg And Raquel Fernández 17 Computational Psycholinguistics 482 Matthew W. Crocker Part IV Applications 515 18 Information Extraction 517 Ralph Grishman 19 Machine Translation 531 Andy Way 20 Natural Language Generation 574 Ehud Reiter 21 Discourse Processing 599 Ruslan Mitkov 22 Question Answering 630 Bonnie Webber And Nick Webb References 655 Author Index 742 Subject Index 763

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Book SynopsisThis comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies Trade Review“The overall evaluation is therefore definitely very good: the work is solid, complete and definitely an important reference for NLP and CL.” (Linguistlist, 14 January 2014) “Altogether, this Handbookcovers a wide variety of topics in NLP and CL and, is of particular use to researchers in the field of MT. On a more general note, graduate students or novice researchers can utilise this book as a comprehensive starting point for their area of interest within NLP or CL … All in all, this is very well compiled book, which effectively balances the width and depth of theories and applications in two very diverse yet closely related fields of language research.” (Machine Translation, 18 March 2012)Table of ContentsList of Figures ix List of Tables xiv Notes on Contributors xv Preface xxiii Introduction 1 Part I Formal Foundations 9 1 Formal Language Theory 11 SHULY WINTNER 2 Computational Complexity in Natural Language 43 IAN PRATT-HARTMANN 3 Statistical Language Modeling 74 CIPRIAN CHELBA 4 Theory of Parsing 105 MARK-JAN NEDERHOF AND GIORGIO SATTA Part II Current Methods 131 5 Maximum Entropy Models 133 ROBERT MALOUF 6 Memory-Based Learning 154 WALTER DAELEMANS AND ANTAL VAN DEN BOSCH 7 Decision Trees 180 HELMUT SCHMID 8 Unsupervised Learning and Grammar Induction 197 ALEXANDER CLARK AND SHALOM LAPPIN 9 Artificial Neural Networks 221 JAMES B. HENDERSON 10 Linguistic Annotation 238 MARTHA PALMER AND NIANWEN XUE 11 Evaluation of NLP Systems 271 PHILIP RESNIK AND JIMMY LIN Part III Domains of Application 297 12 Speech Recognition 299 STEVE RENALS AND THOMAS HAIN 13 Statistical Parsing 333 STEPHEN CLARK 14 Segmentation and Morphology 364 JOHN A. GOLDSMITH 15 Computational Semantics 394 CHRIS FOX 16 Computational Models of Dialogue 429 JONATHAN GINZBURG AND RAQUEL FERNÁNDEZ 17 Computational Psycholinguistics 482 MATTHEW W. CROCKER Part IV Applications 515 18 Information Extraction 517 RALPH GRISHMAN 19 Machine Translation 531 ANDY WAY 20 Natural Language Generation 574 EHUD REITER 21 Discourse Processing 599 RUSLAN MITKOV 22 Question Answering 630 BONNIE WEBBER AND NICK WEBB References 655 Author Index 742 Subject Index 763

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Book SynopsisComputer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features.Table of ContentsChapter 1. Image Capture and RepresentationChapter 2. Image Pre-ProcessingChapter 3. Global and Regional FeaturesChapter 4. Local Feature Design Concepts, Classification, and LearningChapter 5. Taxonomy Of Feature Description AttributesChapter 6. Interest Point Detector and Feature Descriptor SurveyChapter 7. Ground Truth Data, Data, Metrics, and AnalysisChapter 8. Vision Pipelines and OptimizationsAppendix A. Synthetic Feature AnalysisAppendix B. Survey of Ground Truth DatasetsAppendix C. Imaging and Computer Vision ResourcesAppendix D. Extended SDM Metrics

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Book SynopsisAdapts formal knowledge from the field of Conversation Analysis (CA) to the design of natural language interfaces. The book outlines the Natural Conversation Framework (NCF), developed at IBM Research, a systematic framework for designing interfaces that work like natural conversation.Table of Contents Preface Introduction Conversation Analysis Conversation Authoring Natural Conversation Framework Conversational Activity UX Patterns Sequence Management UX Patterns Conversation Management UX Patterns Conversational UX Design Process Conclusion Appendix A Appendix B Appendix C Appendix D References Index Author Biographies

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Book SynopsisCompanies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques

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Book SynopsisAs voice interfaces and virtual assistants have moved out of the industry research labs and into the pockets, desktops and living rooms of the general public, a demand for a new kind of user experience (UX) design is emerging. Although the people are becoming familiar with Siri, Alexa, Cortana and others, their user experience is still characterized by short, command- or query-oriented exchanges, rather than longer, conversational ones. Limitations of the microphone and natural language processing technologies are only part of the problem. Current conventions of UX design apply mostly to visual user interfaces, such as web or mobile; they are less useful for deciding how to organize utterances, by the user and the virtual agent, into sequences that work like those of natural human conversation. This edited book explores the intersection of UX design, of both text- or voice-based virtual agents, and the analysis of naturally occurring human conversation (e.g., the Conversation Analysis, Discourse Analysis and Interactional Sociolinguistics literatures). It contains contributions from researchers, from academia and industry, with varied backgrounds working in the area of human-computer interaction. Each chapter explores some aspect of conversational UX design. Some describe the design challenges faced in creating a particular virtual agent. Others discuss how the findings from the literatures of the social sciences can inform a new kind of UX design that starts with conversation.Table of ContentsConversational UX Design: An Introduction.- Adapting to Customer Initiative: Insights from Human Service Encounters.- Safety First: Conversational agents for Health Care.- Conversational Agents for Physical World Navigation.- Helping Users Reflect on Their Own Heath-related Behaviors.- Teaching Agents When they Fail: End User Development in Goal-oriented Conversational Agents.- Recovering from Dialogue Failures Using Multiple Agents in Wealth Management Advice.- Conversational Style: Beyond the nuts and bolts of conversation.- A natural Conversation Framework for Conventional UX Design.

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Book SynopsisThis book constitutes the refereed proceedings of the 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, which was held in December 2021. Due to COVID-19 pandemic the conference was held virtually.The 17 full, 14 short, and 5 practice papers presented in this volume were carefully reviewed and selected from 87 submissions. The papers were organized in topical sections named: Knowledge Discovery from Digital Collections; Search for Better User Experience; Information Extraction; Multimedia; Text Classification and Matching; Data Infrastructure for Digital Libraries; Data Modeling; Neural-based Learning.

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Book SynopsisArgumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.Table of ContentsPreface.- Acknowledgments.- Introduction.- Argumentative Language.- Modeling Arguments.- Corpus Annotation.- Finding Claims.- Finding Supporting and Objecting Statements.- Deriving the Structure of Argumentation.- Assessing Argumentation.- Generating Argumentative Text.- Summary and Perspectives.- Bibliography.- Authors' Biographies.- Index.

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Book SynopsisEdited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 28th Workshop on Logic, Language, Information and Computation, WoLLIC 2022, Iasi, Romania, in September 2022. The 25 full papers presented included with 8 extra abstracts, 5 invited talks and 3 tutorials were fully reviewed and selected from 46 submissions. The conference aims fostering interdisciplinary research in pure and applied logic.Table of ContentsProof theory,.- Model theory.- Modal and temporal logics.- Automated reasoning.-Constraint and logic programming.- Constructive mathematics.- Equational logic and rewriting.- Finite Model Theory.- Descriptive complexity,.- Higher order logic.- Programming logic.- Model checking.- Type theory.- Lambda calculus.- Semantics of programming languages.- Computational linguistics.-Language and computation.- Logic and language.

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Book SynopsisThis open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary studies from the social sciences and automated approaches, mostly from computer science and computational linguistics, to analyze and reveal media bias. This way, it identifies the strengths and weaknesses of current approaches for identifying and revealing media bias. Chapter 3 discusses the solution design space to address the identified research gap and introduces person-oriented framing analysis (PFA), a new approach to identify substantial frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and frame identification, the first and second component of PFA. Chapter 5 also introduces the first large-scale dataset and a novel model for target-dependent sentiment classification (TSC) in the news domain. Eventually, Chapter 6 introduces Newsalyze, a prototype system to reveal biases to non-expert news consumers by using the PFA approach. In the end, Chapter 7 summarizes the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future research on media bias. This book mainly targets researchers and graduate students from computer science, computational linguistics, political science, and further social sciences who want to get an overview of the relevant state of the art in the other related disciplines and understand and tackle the issue of bias from a more effective, interdisciplinary viewpoint.Table of Contents1. Introduction.- 2. Media Bias Analysis.- 3. Person-Oriented Framing Analysis.- 4. Target Concept Analysis.- 5. Frame Analysis.- 6. Prototype.- 7. Conclusion.

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