Natural language and machine translation Books

Book SynopsisThis best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

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Book SynopsisDescription Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully chosen examples and expand their machine's knowledge which they can then apply to a range of challenges. Key Features • Easy-to-follow • Clear examples • Hands-on-guide Audience A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful. About the technology Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and readers can see examples of it in everything from chatbots to the speech-recognition software on their phone. Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.

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Book SynopsisMaster Java persistence using the industry-leading tools Spring Data and Hibernate. In Java Persistence with Spring Data and Hibernate you will learn: Mapping persistent classes, value types, and inheritance Mapping collections and entity associations Processing transactions with Spring Data and Hibernate Creating fetch plans, strategies, and profiles Filtering data Building Spring Data REST projects Using Java persistence with non-relational databases Querying JPA with QueryDSL Testing Java persistence applications Java Persistence with Spring Data and Hibernate teaches you the ins-and-outs of Java persistence with hands-on examples using Spring Data, JPA and Hibernate. The book carefully analyzes the capabilities of the major Java persistence tools, and guides you through the most common use cases. You'll learn how to make and utilize mapping strategies, and efficiently test Java persistence applications. The practical techniques are demonstrated with both relational and non-relational databases. about the technology Persistence enables an application's data to exist for the long term, even after a program is stopped or terminated. Whether you're saving state from session to session or maintaining long-term records, Java persistence tools like Spring Data, JPA, and Hibernate help deliver the object relational mapping that connects code's objects with your database. about the book Java Persistence with Spring Data and Hibernate explores persistence with the most popular available tools. You'll benefit from detailed coverage of Spring Data JPA, Spring Data JDBC, Spring Data REST, JPA, and Hibernate, comparing and contrasting the alternatives so you can pick what's best for your code. Begin with a hands-on introduction to object-relational mapping (ORM), then dive into mapping strategies for linking up objects and your database. You'll learn about the different approach to transactions for both Hibernate and Spring Data, and even how to deliver Java persistence with non-relational databases. Finally, you'll explore testing strategies for persistent applications to keep your code clean and bug free. Trade Review"Want to learn Java persistence without having to dig through the reference documentation? Read it and you'll know what to do (and what to avoid)." Marcus Geselle "This book is crucial not only for newbies but also for any senior developers working with JVM Persistence." Özay Duman "This book gives a great foundation for working with JPA and Hibernate. If I were to teach the subject, I would not hesitate to use this book." Kim Kjærsulf "Excellent introduction to how Java persistence is handled in the real world." Daniel Carl

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Book SynopsisTackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem.

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Book SynopsisIf you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.

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Book SynopsisWith this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

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Book SynopsisThis accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI-including regression, neural networks, optimization, backpropagation, and Markov chains.

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Book SynopsisThis is an open access title. It is made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International license. It is available to read and download as a PDF version on the Oxford Academic platform.We live in a world where artificial intelligence and intensive use of personal data has become normalized. Companies across the world are developing and launching technologies to infer and interact with emotions, mental states, and human conditions. However, the methods and means of mediating information about people and their emotional states are incomplete and problematic. Automating Empathy offers a critical exploration of technologies that sense intimate dimensions of human life and the modern ethical questions raised by attempts to perform and simulate empathy. It traces the ascendance of empathic technologies from their origins in physiognomy and pathognomy to the modern day and explores technologies in nations with non-Western ethical histories and appTable of ContentsChapter 1: Automating Empathy SECTION I: THEORY AND ETHICS Chapter 2: Hyperreal Emotion Chapter 3: Assessing the Physiognomic Critique Chapter 4: Hybrid Ethics Chapter 5: The Context Imperative: Extractivism, Japan, and Holism SECTION II: APPLICATIONS AND IMPLICATIONS Chapter 6: Positive Education Chapter 7: Automating Vulnerability: Sensing Interiors Chapter 8: Hybrid Work: Automated for the People? Chapter 9: Waveforms of Human Intention: Towards Everyday Neurophenomenology Chapter 10: Selling Emotions: Moral Limits of Intimate Data Markets Chapter 11: Uncertainty for Good: Inverting Automated Empathy References

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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 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 SynopsisTable of Contents1. Linguistics: Core Concepts and Principles 2. Grammars 3. Open-Source Libraries, Application Frameworks, Workflow Systems, and Other Resources 4. Mathematical Essentials 5. Probability 6. Inference and Prediction Methods 7. Random Processes 8. Bayesian Methods 9. Machine Learning 10. Artificial Neural Networks for Natural Language Processing 11. Information Retrieval 12. Language Core Tasks 1 13. Language Core Tasks 2 14. Language Understanding Applications 1 15. Language Understanding Applications 2 16. Deep Learning for Natural Language Processing 17. Text Mining for Modeling Cyberattacks 18. World Languages and Crosslinguistics 19. Linguistic Elegance of the Languages of South India 20. Current Trends and Open Problems

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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 SynopsisThis Element presents an alternative eye tracking methodology for investigating translators' web search behaviour as well as a systematic approach to gauging the reasoning behind translators' highly complex and context-dependent interaction with search engines and the Web.Table of Contents1. Introduction; 2. Existing studies; 3. Methodology; 4. Findings and discussion; 5. Conclusion; References.

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Book SynopsisAdvancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond. Spanning historical context, validity and fairness issues, emerging technologies, and implications for feedback and personalization, these chapters represent the most robust treatment yet about NLP for education measurement researchers, psychometricians, testing profeTable of ContentsPreface by Victoria Yaneva and Matthias von DavierSection I: Automated ScoringChapter 1: The Role of Robust Software in Automated Scoring by Nitin Madnani, Aoife Cahill, and Anastassia LoukinaChapter 2: Psychometric Considerations when Using Deep Learning for Automated Scoring by Susan Lottridge, Chris Ormerod, and Amir JafariChapter 3: Speech Analysis in Assessment by Jared C. Bernstein and Jian ChengChapter 4: Assessment of Clinical Skills: A Case Study in Constructing an NLP-Based Scoring System for Patient Notes by Polina Harik, Janet Mee, Christopher Runyon, and Brian E. ClauserSection II: Item DevelopmentChapter 5: Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks by Ruslan Mitkov, Le An Ha, Halyna Maslak, Tharindu Ranasinghe, and Vilelmini SosoniChapter 6: Training Optimus Prime, M.D.: A Case Study of Automated Item Generation using Artificial Intelligence – From Fine-Tuned GPT2 to GPT3 and Beyond by Matthias von DavierChapter 7: Computational Psychometrics for Digital-first Assessments: A Blend of ML and Psychometrics for Item Generation and Scoring by Geoff LaFlair, Kevin Yancey, Burr Settles, Alina A von DavierSection III: Validity and FairnessChapter 8: Validity, Fairness, and Technology-based Assessment by Suzanne LaneChapter 9: Evaluating Fairness of Automated Scoring in Educational Measurement by Matthew S. Johnson and Daniel F. McCaffreySection IV: Emerging TechnologiesChapter 10: Extracting Linguistic Signal from Item Text and Its Application to Modeling Item Characteristics by Victoria Yaneva, Peter Baldwin, Le An Ha, and Christopher RunyonChapter 11: Stealth Literacy Assessment: Leveraging Games and NLP in iSTART by Ying Fang, Laura K. Allen, Rod D. Roscoe, and Danielle S. McNamaraChapter 12: Measuring Scientific Understanding Across International Samples: The Promise of Machine Translation and NLP-based Machine Learning Technologies by Minsu Ha and Ross H. NehmChapter 13: Making Sense of College Students’ Writing Achievement and Retention with Automated Writing Evaluation by Jill Burstein, Daniel McCaffrey, Steven Holtzman & Beata Beigman KlebanovContributor Biographies

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Book SynopsisThis collection surveys the state of the art of computer-assisted literary translation (CALT), making the case for its potential to enhance literary translation research and practice.The volume brings together early career and established scholars from around the world in countering prevailing notions around the challenges of effectively implementing contemporary CALT applications in literary translation practice which has traditionally followed the model of a single translator focused on a single work. The book begins by addressing key questions on the definition of literary translation, examining its sociological dimensions and individual translator perspective. Chapters explore the affordances of technological advancements and availability of new tools in such areas as post-edited machine translation (PEMT) in expanding the boundaries of what we think of when we think of literary translation, looking to examples from developments in co-translation, collaborative translation, crowd-sourced translation and fan translation. As the first book of its kind dedicated to the contribution CALT in its various forms can add to existing and future scholarship, this volume will be of interest to students and scholars in Translation Studies, especially those working in literary translation, machine translation and translation technologies.Table of ContentsIntroduction ANDREW ROTHWELL, ANDY WAY AND ROY YOUDALEPart 1: The Automated and Post-Edited Machine Translation of Literature1 Literary-Adapted Machine Translation in a Well-Resourced Language Pair: Explorations with More Data and Wider ContextsANTONIO TORAL, ANDREAS VAN CRANENBURGH, AND TIA NUTTERS2 ‘I Am a Bit Surprised’: Literary Translation and Post-Editing Processes CompareWALTRAUD KOLB3 Mark My Keywords: A Translator-Specific Exploration of Style in Literary Machine TranslationMARION WINTERS AND DOROTHY KENNYPart 2: Machine Translation Applications in Literary Translation4 MT and CAT: Challenges, Irrelevancies or Opportunities for Literary Translation?JAMES LUKE HADLEY5 Retranslating Proust Using CAT, MT and Other ToolsANDREW ROTHWELL6 Author-Tailored Neural Machine Translation Systems for Literary WorksANTONI OLIVER7 Machine Translation of Chinese Fantasy (Xianxia) Novels: An Investigation Into the Leading Websites Translating Chinese Internet Literature Into EnglishSHUYIN ZHANG8 Up and About, or Betwixt and Between?: The Poetry of a Translation MachineTIM VAN DE CRUYS9 Metaphor in Literary Machine Translation: Style, Creativity and LiterarinessALETTA G. DORSTPart 3: Corpus Linguistics, Text-Visualisation and Literary Translation10 KonText in Trilingual Studies—Supporting Phraseology Translation Based on the EPB CorpusANGELIKA PELJAK-ŁAPIŃSKA11 Voyant Tools’ Little Outing: How a Text Reading and Analysis Environment Can Help Literary TranslatorsLISA HORENBERG12 (Re)creating Equivalence of Stylistic Effect: A Corpus-Aided MethodologyTEREZA ŠPLÍCHALOVÁPart 4: Applying Specialised Electronic Tools to Literary Translation13 The ExperimentAVRAHAM J. ROOS14 Augmenting and Informing the Translation Process through Workflow-Enabled CALT ToolsSASHA MILE RUDAN, EUGENIA KELBERT, LAZAR KOVACEVIC, MATTHEW REYNOLDS, AND SINISHA RUDAN

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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 SynopsisTable of ContentsList of Figures xi About the Companion Website xii 1 Introduction 1 1.1 Why Program? Why Python? 1 1.2 Course Overview and Aims 4 1.3 A Brief Note on the Exercises 5 1.4 Conventions Used in this Book 6 1.5 Installing Python 6 1.5.1 Installing on Windows 6 1.5.2 Installing on the Mac 7 1.5.3 Installing on Linux 8 1.6 Introduction to the Command Line/Console/Terminal 8 1.6.1 Activating the Command Line on Windows 9 1.6.2 Activating the Command Line on the Mac or Linux 9 1.7 Editors and IDEs 10 1.8 Installing and Setting Up WingIDE Personal 10 1.9 Discussions 11 2 Programming Basics I 15 2.1 Statements, Functions, and Variables 15 2.2 Data Types – Overview 17 2.3 Simple Data Types 18 2.3.1 Strings 18 2.3.2 Numbers 20 2.3.3 Binary Switches/Values 21 2.4 Operators – Overview 21 2.4.1 String Operators 21 2.4.2 Mathematical Operators 22 2.4.3 Logical Operators 24 2.5 Creating Scripts/Programs 25 2.6 Commenting Your Code 26 2.7 Discussions 28 3 Programming Basics II 33 3.1 Compound Data Types 33 3.2 Lists 35 3.3 Simple Interaction with Programs and Users 37 3.4 Problem Solving and Damage Control 38 3.4.1 Getting Help from Your IDE 38 3.4.2 Using the Debugger 39 3.5 Control Structures 40 3.5.1 Conditional Statements 41 3.5.2 Loops 42 3.5.3 while Loops 43 3.5.4 for Loops 44 3.5.5 Discussions 45 4 Intermediate String Processing 53 4.1 Understanding Strings 53 4.2 Cleaning Up Strings 54 4.3 Working with Sequences 55 4.3.1 Overview 55 4.3.2 Slice Syntax 56 4.4 More on Tuples 57 4.5 ‘Concatenating’ Strings More Efficiently 59 4.6 Formatting Output 60 4.6.1 Using the % Operator 60 4.6.2 The format Method 61 4.6.3 f- Strings 61 4.6.4 Formatting Options 62 4.7 Handling Case 62 4.8 Discussions 63 5 Working with Stored Data 71 5.1 Understanding and Navigating File Systems 71 5.1.1 Showing Folder Contents 72 5.1.2 Navigating and Creating Folders 74 5.1.3 Relative Paths 75 5.2 Stored Data 76 5.3 Opening and Closing Files 76 5.3.1 File Opening Modes 77 5.3.2 File Access Options 77 5.4 Reading File Contents 78 5.5 Error Handling 79 5.6 Writing to Files 82 5.7 Working with Folders and Paths 83 5.7.1 The os Module 83 5.7.2 The Path Object of the libpath Module 84 5.8 Discussions 86 6 Recognising and Working with Language Patterns 93 6.1 The re Module 93 6.2 General Syntax 94 6.3 Understanding and Working with the Match Object 94 6.4 Character Classes 96 6.5 Quantification 97 6.6 Masking and Using Special Characters 98 6.7 Regex Error Handling 98 6.8 Anchors, Groups and Alternation 99 6.9 Constraining Results Further 101 6.10 Compilation Flags 101 6.11 Discussions 102 7 Developing Modular Programs 109 7.1 Modularity 109 7.2 Dictionaries 109 7.3 User- defined Functions 111 7.4 Understanding Modules 112 7.5 Documenting Your Module 115 7.6 Installing External Modules 116 7.7 Classes and Objects 117 7.7.1 Methods 118 7.7.2 Class Schema 118 7.8 Testing Modules 119 7.9 Discussions 120 8 Word Lists, Frequencies and Ordering 129 8.1 Introduction to Word and Frequency Lists 129 8.2 Generating Word Lists 129 8.3 Sorting Basics 130 8.4 Generating Basic Word Frequency Lists 131 8.5 Lambda Functions 132 8.6 Discussions 134 9 Interacting with Data and Users Through GUIs 143 9.1 Graphical User Interfaces 143 9.2 PyQt Basics 144 9.2.1 The General Approach to Designing GUI- based Programs 144 9.2.2 Useful PyQt Widgets 145 9.2.3 A Minimal PyQt Program 146 9.2.4 Deriving from a Main Window 148 9.2.5 Working with Layouts 148 9.2.6 Defining Widgets and Assigning Layouts 150 9.2.7 Widget Properties, Methods and Signals 150 9.2.8 Adding Interactive Functionality 152 9.3 Designing More Advanced GUIs 153 9.3.1 Actions 153 9.3.2 Creating Menus, Tool and Status Bars 153 9.3.3 Working with Files and Folder in PyQt 155 9.4 Discussions 159 10 Web Data and Annotations 171 10.1 Markup Languages 171 10.2 Brief Intro to HTML 172 10.3 Using the urllib.request Module 174 10.4 Extracting Text from Web Pages 177 10.5 List and Dictionary Comprehension 178 10.6 Brief Intro to XML 179 10.7 Complex Regex Replacements Using Functions 182 10.8 Brief Intro to the TEI Scheme 182 10.8.1 The Header 183 10.8.2 The Text Body 184 10.9 Discussions 188 11 Basic Visualisation 201 11.1 Using Matplotlib for Basic Visualisation 201 11.2 Creating Word Clouds 207 11.3 Filtering Frequency Data Through Stop- Words 208 11.4 Working with Relative Frequencies 210 11.5 Comparing Frequency Data Visually 212 11.6 Discussions 216 12 Conclusion 227 Appendix – Program Code 231 Index 273

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Book SynopsisFrom tech giants to plucky startups, the world is full of companies boasting that they are on their way to replacing human interpreters, but are they right? Interpreters vs Machines offers a solid introduction to recent theory and research on human and machine interpreting, and then invites the reader to explore the future of interpreting. With a foreword by Dr Henry Liu, the 13th International Federation of Translators (FIT) President, and written by consultant interpreter and researcher Jonathan Downie, this book offers a unique combination of research and practical insight into the field of interpreting.Written in an innovative, accessible style with humorous touches and real-life case studies, this book is structured around the metaphor of playing and winning a computer game. It takes interpreters of all experience levels on a journey to better understand their own work, learn how computers attempt to interpret and explore possible futures for human interpreters. <Trade ReviewJonathan Downie continues his mission to bring interpreting research to the people. Outspokenly, he tackles fundamental questions for interpreters in the 21st Century. Firmly grounded in Interpreting Studies, Downie interlaces research with anecdotes well-founded in any interpreter’s daily life. It is an equally trailblazing and sulphurous book on the aspirations of machine interpreting, and the fatal mistake of not making a difference. The book is a welcome addition both to the debate on the future of interpreting and to my students’ literature list. Elisabet Tiselius, Stockholm University, SwedenA deep exploration of the limits of language, technology and the enabling power of human mediation in promoting understanding. This book puts interpreters back in the driver's seat, where they belong.Ewandro Magalhaes, Technology Advocate and Former Chief Interpreter in the UN System, USATable of ContentsIntroductionLevel One – The fundamentalsChapter 1: What is interpreting?Chapter 2: How humans interpretChapter 3: How computers "interpret"Level Two – How machines gained the upper handChapter 4: How we wrecked our own PRChapter 5: Speech translation's marvellous (but misleading) marketing Level Three – Choose your interpreting futureChapter 6: Human interpreting as a stopgapChapter 7: Hanging on with legal help Chapter 8: Mastering niches Chapter 9: Making interpreting matter againLevel Four – Interpreting that beats the botsChapter 10: Beating the bots Stage One: taking back interpreting PRChapter 11: Marketing interpreting that mattersChapter 12: Deliver more than wordsChapter 13: Coaching and supervisionLevel Five – One last thoughtChapter 14: It's time to call a truceBibliographyIndex

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Book SynopsisLearn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you''ll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You''ll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assist

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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 SynopsisThis book will show how generative technology works and the drivers. It will also look at the applications showing what various startupsand large companies are doing in the space.There will also be a look at the challenges and risk factors. During the past decade, companies have spent billions on AI. But the focus has been on applying the technology to predictions which is known as analytical AI. It can mean that you receive TikTok videos that you cannot resist. Or analytical AI can fend against spam or fraud or forecast when a package will be delivered. While such things are beneficial, there is much more to AI. The next megatrend will be leveraging the technology to be creative. For example, you could take a book and an AI model will turn it into a movie at very little cost. This is all part of generative AI. It's still in the nascent stages but it is progressing quickly. Generative AI can already create engaging blog posts, social media messages, beautiful artwork and compellinTable of ContentsChapter 1: Introduction to Generative AI.- Chapter 2: Data.- Chapter 3: AI Fundamentals.- Chapter 4: Core Generative AI Technology.- Chapter 5: Large Language Models.- Chapter 6: Auto Code Generation.- Chapter 7: The Transformation of Business.- Chapter 8: The Impact on Major Businesses.- Chapter 9: The Future.

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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 SynopsisThis volume is an outgrowth of the second Workshop on Logic, Language and Computation held at Stanford in the spring of 1993. The workshop brought together researchers interested in natural language to discuss the current state of the art at the borderline of logic, linguistics and computer science. The papers in this collection fall into three central research areas of the nineties, namely quantifiers, deduction, and context. Each contribution reflects an ever-growing interest in a more dynamic approach to meaning, which focuses on inference patterns and the interpretation of sentences in the context of a larger discourse. The papers apply either current logical machinery - such as linear logic, generalised quantifier theory, dynamic logic - or formal analyses of the notion of context in discourse to classical linguistic issues, with original and thought-provoking results deserving of a wide audience.Table of Contents1. The Context-Dependency of Implicit Arguments; 2. A Deductive Account of Quantification in LFG; 3. The Sorites Fallacy and the Context-dependence of Vague Predicates; 4. Presuppositions and Information Updating; 5. Indefeasible semantics and Defeasible Pragmatics; 6. Pronoun Interpretation Preferences: an Account; 7. Resumptive Quantifiers in Exception Sentences; 8. (In)definites and genericity.

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Book SynopsisThis book pursues the recent upsurge of research in the interface of logic, language and computation, with applications to artificial intelligence and machine learning. It contains a variety of contributions to the logical and computational analysis of natural language. A wide range of logical and computational tools are employed and applied to such varied areas as context-dependency, linguistic discourse, and formal grammar. The papers in this volume cover: context-dependency from philosophical, computational, and logical points of view; a logical framework for combining dynamic discourse semantics and preferential reasoning in AI; negative polarity items in connection with affective predicates; Head-Driven Phrase Structure Grammar from a perspective of type theory and category theory; and an axiomatic theory of machine learning of natural language with applications to physics word problems.Table of ContentsPreface; 1. Indexicals, contexts, and unarticulated constituents; 2. Formalizing context (expanded notes); 3. Changing contexts and shifting assertions; 4. Discourse preferences in dynamic logic; 5. Polarity, predicates and monotonicity; 6. Machine learning of physics word problems.

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Book SynopsisHannah Silva's My Child, the Algorithm, is one of the best books I read this year. Merging the cozy familiarity of child-rearing with the mysterious tension of AI {...}, she has created a new genre of personal narrative, and a story whose grief, hope and curiosity takes on poetic, spiritual dimensions, even when exploring the most common chambers of the human heart. —Michelle Tea, author, Knocking Myself UpMy Child, the Algorithm tells a story of finding joy after betrayal. Like a male seahorse, Hannah Silva carried a baby made from her partner's egg. But when she gave birth, her partner left, and Hannah found herself navigating life alone with her child.Hannah started playing with a precursor to ChatGPT—wondering what AI could tell us about love. To her surprise, she was moved by the results. The algorithm prompted Hannah to share her explorations of dating, sex, friendship, and life as a queer parent in London. With the help and disruption of two unreliable narrators, a toddler and an algorithm, Hannah deconstructs her story, unraveling everything she has been taught to want, and finds alternative ways of thinking, loving, and parenting today.

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Book SynopsisThis book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented.Table of Contents Preface Semantic Roles Available Lexical Resources Machine Learning for Semantic Role Labeling A Cross-Lingual Perspective Summary

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Book SynopsisHumans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning • Models for textual similarity • Deep memory-based NLP • Semantic role labeling • Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He’s the technical coordinator of two large European Union-funded research security-related projects. He’s currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

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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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