Natural language and machine translation Books

Book SynopsisRoberto Infante is a veteran software engineer and quant leader known for turning complex ideas into reliable systems. With decades in high-stakes finance and emerging AI projects, Roberto brings clarity, rigor, and real-world wisdom to every page. He distills deep expertise into practical examples that help developers ship AI features with confidence. 

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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 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 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 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 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 SynopsisLearn how to use ChatGPT, Bing Chat / Copilot, and Bard to get the most out of these powerful tools. ChatGPT has become a tool hundreds of millions use every day - yet few have mastered the art of sweet-talking these new AI chatbots into providing detailed and accurate responses to user prompts. It's vital for every professional tech user to have an understanding of how AI chatbots work, and how best to put them to work. While the fear is that AI will take people’s jobs, it is more likely to be someone using AI that will! Getting Started with ChatGPT and AI Chatbots explores the ‘big three’ AI chatbots - OpenAI ChatGPT, Microsoft / Windows Copilot and Google Bard - demystifying their operation, and providing a wealth of tools for thinking about how to talk to these smart tools. Whether you're a business user, a tech student, or a curious professional, this book is for you to understand and demystify Large Language Models (LLMs) and generative AI tools, harnessing them to enhance your role.Trade ReviewIn this ever so timely book, one of my favourite technologists Mark Pesce, takes us through and behind the screen to explain how the latest generation of AI chatbots actually work. But this is not a book about chatbots, this is a book about how to use AI-powered chatbots in daily life, and how to use them to their fullest potential. -- Genevieve Bell, The Australian National UniversityThis book is a must-read for anyone considering using generative AI in business. Authored in non-technical language, it walks through the origins of the first chatbot into the current generative AI landscape including ChatGPT, Copilot and Bard. Furthermore, the guidance on effective prompting to achieve the right results, and a list of critical dos and don'ts to protect sensitive data really make this an all-round winner. Top marks BCS, highly recommended! -- Pauline Norstrom, Founder and CEO, Anekanta® Consulting and Anekanta® AIThis book provides practical guidance on the use of a rapidly growing technologies referred to as AI chatbots, that offer a substantial productivity advantage to those adept at crafting precise prompts and understanding their diverse applications. It is a valuable resource for mastering these emerging tools, ensuring not only competitiveness but also enabling a focus on the creative and compelling facets of one's work. I highly recommend everyone to read this book and glean insights on wielding this potent power proficiently to remain competent. -- Rashik Parmar MBE FBCS, Group CEO, BCS, The Chartered Institute for ITIn late November 2022, ChatGPT introduced a major advance in Artificial Intelligence that surprised almost everyone, including many experts in AI. Trained on text from billions of books and webpages, it can thoughtfully answer questions on a huge variety of topics and languages. The increase in our capability to access human knowledge is analogous to the increase provided by the internet in 1993. For both technologies, getting good answers requires knowing how to ask the right questions. In this timely and important book, Mark Pesce, who has been at the forefront of new digital technologies for over 30 years, provides a compelling and comprehensive introduction to ChatGPT and how to use it. -- Professor Ken Goldberg, William S. Floyd Jr. Distinguished Chair in Engineering, UC BerkeleyEverything you need to educate yourself and your colleagues about the latest hot topic is found within these pages, it’s a must share for colleagues to stimulate conversations about how your business can best adopt AI safely and where you will find business cases. From useful tips on prompts to security concerns about data, biases, and hallucinations with comparisons, get the facts you need on AI here. These are exactly the kinds of conversations I’m having two or three times a day, helping organisations bring their AI aspirations in line with what their business needs are. -- David Starkings, AI Adoption Consultant, tts digital adoption solutionsGetting Started with ChatGPT and AI Chatbots is perfectly targeted to give an introduction to ChatGPT and AI Chatbots, it compares and contrasts a range of the major options and covers how each of the competing Chatbots can answer the same question very differently. It also poses some interesting thoughts around ‘Prompt Engineering’ and how this may become a whole new skillset that people need to learn. I thought that Chapter 6 brought insight to ‘hallucinations’ and how AI chatbots have the ability to sound very confident, even when they are wrong, the importance of fact checking and using human experts is stressed well. -- Richard Parker MBCS, Chair, AELP Sector Forum, IT & DigitalMark's book is a practical and pragmatic guide to contemporary spellcasting - the magic needed to evoke useful, safe and factual results from the emerging field of large language models. Importantly the book provides the framework needed to evaluate LLM's critically and ground them in reality - a must-have resource for explorers in this field. -- Bhautik Joshi, Principal Applied Scientist, CanvaEverybody is talking about AI, and soon it will be integrated into every part of our electronic devices - always on and always available. Getting Started with ChatGPT and AI Chatbots explains in an easy-to-understand way how AI works and how to get the best results from it, safely and securely. -- David Smith MBCS MIET, Lead Business Analyst, Lloyds Banking GroupWith this book, Mark creates a path to a dialog with new and emerging AI platforms, one that I’ll be referring to again and again as these technologies evolve. Ever heard of autonomous agents? You have now. A brilliant, timely, and superbly helpful pathfinder. -- Dr Catherine Ball PhD DSc GAICD, CompIEAust, Associate Professor, The Australian National UniversityAn essential read for those venturing into Generative AI, this book seamlessly blends historical and theoretical insights with practical examples. The book adeptly navigates concerns surrounding Generative AI, making it a valuable resource for students, academics, and professionals alike. -- Professor Lasith Gunawardena FBCS, Department Head of Information Technology, University of Sri Jayewardenepura, Sri LankaIf you’ve only dabbled or toyed with ChatGPT or other generative AI tools, you’ll find Mark Pesce’s book invaluable. The book will educate you about how to use AI chatbots effectively and safely. It covers the basics of various chatbots, the technology behind them, and the concept of 'prompt engineering' to elicit desired responses. It also addresses safety and security concerns and explores advanced techniques like using personas and chain-of-reason prompts. Highly recommended! -- Tim Clements FBCS CITP FIP CIPP/E CIPM CIPT, Business Owner, Purpose and MeansA concise yet thorough take on AI and how to use it to its advantage. Taking us from our place of trust in the output of computers to encourage us think further about what we input, and to question the completion. ‘If you wouldn’t shout it from the rooftop, you shouldn’t type it into a chatbot.’ -- Kym Glover CITP MBCS MAPM, Program Manager, ForgeRock.Not yet started with an AI chatbot? This book is your call-to-action and your how-to rolled into one. A smooth and informative read that'll kickstart your practical learning and give you great ideas to get the best out of Generative AIs. A real confidence builder. -- Bronia Anderson-Kelly, IT Change Consultant, Sabiduria LtdThis book is a fantastic resource for anyone starting out with Gen AI tools. It not only covers the fundamental aspects of the leading Gen AI tools available today, but also the essentials of prompt engineering. With the widespread adoption of Gen AI in the coming years, just like the internet, this book is a useful guide. Mark not only provides a solid foundation in the basics but also delivers valuable insights into the history and potential future of these tools. Highly recommended for its clarity and depth. -- Graeme Vermeulen, Head of Technical Architecture, AdvancedTable of ContentsIntroduction 1. Getting Started 2. How AI Chatbots Work 3. Security and Privacy 4. Simple Prompts 5. Reasoning and Summarising 6. Truthiness and Chatbots 7. Character, Context and Conflict 8. Using Character, Context and Challenge to Craft Powerful Prompts 9. Chain of Thought Prompts 10. Computer Says No 11. Creating Images with Bing Chat 12. Windows Copilot 13. Autonomous Agents 14. Will and AI Chatbot Take My Job 15. What the Future Holds / Next Steps

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Book Synopsis Numa Dhamani is a natural-language-processing researcher known for translating complex language science into actionable insight. With years at the intersection of technology and society, Numa brings clarity, curiosity, and social awareness to every page. She distills her expertise into practical guidance that empowers readers to explore AI responsibly.?  Maggie Engler is an engineer and safety researcher working on large language models at a pioneering AI lab. With deep experience in measuring harms and building safeguards, Maggie offers balanced, evidence-based advice wrapped in an empathetic voice. She turns cutting-edge research into usable frameworks that help readers champion trustworthy AI systems.

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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 SynopsisNicole Koenigstein is a CEO and Chief AI Officer renowned for transforming raw research into profitable AI systems. With years leading Quantmate's agentic intelligence platform, Nicole brings clarity, precision, and business focus to every page. She distills deep model-building expertise into accessible guidance that helps readers deliver faster, smarter transformer solutions. 

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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 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 book introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, the and the nature of language. The book also emphasizes the portions of mathematics needed to understand the discussed algorithms. Table of ContentsPreface, PART I: Introduction, 1. The Nature of Language, PART II: Mathematics, 2. Relations, 3. Algebraic Structures, 4. Linear Algebra, 5. Conceptual Knowledge Processing and Formal Concept Analysis, PART III: Knowledge Representation for NLP, 6. Measuring Word Meaning Similarity, 7. Semantics and Query Languages, 8. Multi-Lingual Querying and Parametric Theory, PART IV: Knowledge Extraction and Engineering for NLP, 9. Word Sense Disambiguation, 10. Text Entailment, 11. Text Segmentation, 12. Text Summarization, 13. Named Entity Recognition, Bibliography, Index

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Book SynopsisPerspectives on Artificial Intelligence in Times of Turbulence: Theoretical Background to Applications offers a comprehensive exploration of the intricate relationship between artificial intelligence (AI) and the ever-changing landscape of our society. The book defines AI as machines capable of performing tasks that were once exclusive to human cognition. However, it emphasizes the current limitations of AI, dispelling the notion of sophisticated cyborgs depicted in popular culture. These machines lack self-awareness, struggle with understanding context—especially in language—and are constrained by historical data and predefined parameters. This distinction sets the stage for examining AI's impact on the job market and the evolving roles of humans and machines. Rather than portraying AI as a threat, this book highlights the symbiotic relationship between humans and machines. It recognizes that while certain jobs may become obsolete, new opportunities will emerge. The unique abilities of human beings—such as relational skills, emotional intelligence, adaptability, and understanding of differences—will continue to be indispensable in a rapidly transforming society. The book further explores key objectives and strategies for organizations navigating the AI-driven landscape. From maintaining focus on strategic goals to adapting to new productivity paradigms, from fostering effective communication to promoting feedback and continuous improvement, the chapters provide practical insights and methodologies for managing change and harnessing AI's potential. Its perspectives cover a wide range of topics such as business sustainability, change management, cybersecurity, digital economy and transformation, information systems management, management models and tools, and continuous improvement are comprehensively addressed. Additionally, the book delves into healthcare, telemedicine, Health 4.0, privacy and security, knowledge management, learning, and presents real-world case studies. Designed for researchers and professionals seeking to enhance their knowledge and research capabilities, this book offers a consistent theoretical and practical foundation. It serves as a springboard for further studies, supports change management initiatives within organizations, and facilitates knowledge sharing among experts. This book is an essential companion for colleges with master's and Ph.D. degree investigators, and researchers across a wide range of disciplines.

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Book SynopsisIn the global research community, English has become the main language of scholarly publishing in many disciplines. At the same time, online machine translation systems have become increasingly easy to access and use. Is this a researcher’s match made in heaven, or the road to publication perdition? Here Lynne Bowker and Jairo Buitrago Ciro introduce the concept of machine translation literacy, a new kind of literacy for scholars and librarians in the digital age. For scholars, they explain how machine translation works, how it is (or could be) used for scholarly communication, and how both native and non-native English-speakers can write in a translation-friendly way in order to harness its potential. Native English speakers can continue to write in English, but expand the global reach of their research by making it easier for their peers around the world to access and understand their works, while non-native English speakers can write in their mother tongues, but leverage machine translation technology to help them produce draft publications in English. For academic librarians, the authors provide a framework for supporting researchers in all disciplines as they grapple with producing translation-friendly texts and using machine translation for scholarly communication—a form of support that will only become more important as campuses become increasingly international and as universities continue to strive to excel on the global stage. Machine Translation and Global Research is a must-read for scientists, researchers, students, and librarians eager to maximize the global reach and impact of any form of scholarly work.Trade ReviewBowker and Ciro describe ways that machine translation is used in the context of scholarly communication, and suggest how to use it more effectively. Getting an online machine translation is easy, they say, but it is often more complex to make critical and effective use of a machine translation as part of the scholarly communication process. They cover scholarly communication, machine translation, expanding the reach of knowledge through translation-friendly writing, some wider implications of using machine translation for scholarly communication, and towards a framework for machine translation literacy. -- Annotation ©2019 * (protoview.com) *Table of ContentsIntroduction Chapter 1. Scholarly Communication Chapter 2. Machine Translation Chapter 3. Expanding the Reach of Knowledge through Translation-Friendly Writing Chapter 4. Some Wider Implications of Using Machine Translation for Scholarly Communication Chapter 5. Towards a Framework for Machine Translation Literacy

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Book SynopsisDiscover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them.This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is forThis book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.Table of ContentsTable of Contents Revisiting Deep Learning architecture and techniques Deep Neural Networks for multiclass classification Deep Neural Networks for regression Image classification and recognition Image classification using convolutional neural networks Applying Autoencoder neural networks using Keras Image classification for small data using transfer learning Creating new images using generative adversarial networks Deep network for text classification Text classification using recurrent neural networks Text classification using Long Short-Term Memory Network Text classification using convolutional recurrent networks Tips, tricks and the road ahead

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Book SynopsisThis book contains a comprehensive treatment of advanced LaTeX features. The focus is on the development of high quality documents and presentations, by revealing powerful insights into the LaTeX language. The well-established advantages of the typesetting system LaTeX are the preparation and publication of platform-independent high-quality documents and automatic numbering and cross-referencing of illustrations or references. These can be extended beyond the typical applications, by creating highly dynamic electronic documents. This is commonly performed in connection with the portable document format (PDF), as well as other programming tools which allow the development of extremely flexible electronic documents.Trade Review“This book can serve as a guide for long-term users of the language in any discipline. Along with online resources, any professor or researcher could benefit from having this book in their library as a reference guide when writing and editing a lengthy work, like a book or an article collection, or when preparing presentations and posters. … Finally, teachers and instructors, whether in higher or secondary education, will find useful information for preparing consistent exam tests and textbooks.” (Lazaros Moysis, zbMATH 1491.68005, 2022)Table of ContentsIntroduction: The Basics.- Advanced Formatting.- Floating Objects.- Presentations.- Exams (Tests, Quizzes).- E-Learning: Blended Learning and Flipped Classroom Support.

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Book Synopsis​This book constitutes the refereed proceedings of the 11th Conference on Artificial Intelligence and Natural Language, AINL 2022, held in St. Petersburg, Russia, in April 2022. The 8 revised full papers and 1 short paper were carefully reviewed and selected from 20 submissions. The volume presents recent research in areas of of text mining, speech technologies, dialogue systems, information retrieval, machine learning, articial intelligence, and robotics. Table of ContentsInferring image background from text description.- Topical Extractive Summarization.- The Semantic Shifts of the Topical Structure in the Corpus of Lentach News Posts.- Development of folklore motif classifie using limited data.- Morphological and Emotional Features of the Speech in Children with Typical Development, Autism Spectrum Disorders and Down Syndrome.- WikiMulti: a Corpus for Cross-Lingual Summarization.- Rethinking Crowd Sourcing for Semantic Similarity.- Interplay of Visual and Acoustic Cues of Irony Perception: a Case Study of Actor’s Speech.- Findings of Biomedical Russian to English Machine Translation Competition.- Translation of medical texts with ensembling and knowledge distillation.

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Book SynopsisFrom proposal to examination, producing a dissertation or thesis is a challenge. Grounded in decades of experience with research training and supervision, this fully updated and revised edition takes an integrated, down-to-earth approach drawing on case studies and examples to guide you step-by-step towards productive success.Early chapters frame the tasks ahead and show you how to get started. From there, practical advice and illustrations take you through the elements of formulating research questions, working with software, and purposeful writing of each of the different kinds of chapters, and finishes with a focus on revision, dissemination and deadlines. How to Write a Better Thesis presents a cohesive approach to research that will help you succeed. Trade ReviewFrom the book reviews:“After reading the book, you are left with no doubt as to what is required to write a thesis, as well as how to undertake the task using a systematic approach. … It should be mandatory reading for all postgraduate students embarking on a master’s degree or higher academic qualification. I highly recommend it.” (S. M. Godwin, Computing Reviews, August, 2014)Table of ContentsWhat is a Thesis?.- Thesis Structure.- Mechanics of Writing.- Making a Strong Start.- The Introductory Chapter.- Background Chapters.- Establishing Your Contribution.- Outcomes and Results.- The Discussion or Interpretation.- The Conclusion.- Before You Submit.- Beyond the Thesis.

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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 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 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 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 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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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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