'Natural Language Processing in the Real World' is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is situated in the real-world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling, and classic Machine Learning to Deep Learning and Transformers, several popular applications of NLP are discussed and implemented. This book provides a hands-on and holistic guide for anyone looking to build NLP solutions, from students of Computer Science to those involved in large-scale industrial projects. .

*The most comprehensive up-to-date student-friendly guide to translation tools and technologies *Translation Tools and Technologies are an essential component of any translator training programme, following European Masters in Translation framework guidelines *Unlike the competition, this textbook offers comprehensive and accessible explanations of how to use current translation tools, illustrated by examples using a wide range of languages, linked to task-oriented, self-study training materials

*The most comprehensive up-to-date student-friendly guide to translation tools and technologies *Translation Tools and Technologies are an essential component of any translator training programme, following European Masters in Translation framework guidelines *Unlike the competition, this textbook offers comprehensive and accessible explanations of how to use current translation tools, illustrated by examples using a wide range of languages, linked to task-oriented, self-study training materials

The Routledge Handbook of Corpus Linguistics 2e provides an updated overview of a dynamic and rapidly growing area with a widely applied methodology. Over a decade on from the first edition of the Handbook, this collection of 47 chapters from experts in key areas offers a comprehensive introduction to both the development and use of corpora as well as their ever-evolving applications to other areas, such as digital humanities, sociolinguistics, stylistics, translation studies, materials design, language teaching and teacher development, media discourse, discourse analysis, forensic linguistics, second language acquisition and testing. The new edition updates all core chapters and includes new chapters on corpus linguistics and statistics, digital humanities, translation, phonetics and phonology, second language acquisition, social media and theoretical perspectives. Chapters provide annotated further reading lists and step-by-step guides as well as detailed overviews across a wide range of themes. The Handbook also includes a wealth of case studies that draw on some of the many new corpora and corpus tools that have emerged in the last decade. Organised across four themes, moving from the basic start-up topics such as corpus building and design to analysis, application and reflection, this second edition remains a crucial point of reference for advanced undergraduates, postgraduates and scholars in applied linguistics.

Serious work using computers to support language teaching and learning began in the 1960s, but it was not until the beginning of the 1980s when microcomputers began to proliferate that groups of practitioners began forming professional groups and a formal identification of the field occurred. Although the early promise of computer-assisted language learning (or ?CALL?), to revolutionize second-language learning has not been met, the past quarter century has seen a fascinating range of growth. This is not only because of lessons learned from research and practice, but also due to the rapid and continuing shifts in the technology itself.

Nominally a branch of applied linguistics, 'CALL' is truly interdisciplinary, drawing its core concepts not only from linguistics, but also from computer science, speech engineering, psychology, sociology, second-language acquisition, and general education.

This new four-volume title from Routledge will allow 'CALL' practitioners, researchers, and students to easily access the best and most influential foundational and cutting-edge scholarship. The is also a comprehensive introduction to critical concepts in 'CALL' for applied linguists and language educators interested in the growing role of technology in second-language acquisition.

A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.

Innovative examination of augmentation technologies in terms of technical, social, and ethical considerations Usable as a supplemental text for a variety of courses, and also of interest to researchers and professionals in fields including: technical communication, digital communication, UX design, information technology, informatics, human factors, artificial intelligence, ethics, philosophy of technology, and sociology of technology First major work to combine technological, ethical, social, and rhetorical perspectives on human augmentation Additional cases and research material available at the authors' Fabric of Digital Life research database at https://fabricofdigitallife.com/

This concise volume offers an accessible introduction to state-of-the-art artificial intelligence (AI) language models, providing a platform for their use in textual interpretation across the humanities and social sciences. The book outlines the affordances of new technologies for textual analysis, which has historically employed established approaches within the humanities. Neuman, Danesi, and Vilenchik argue that these different forms of analysis are indeed complementary, demonstrating the ways in which AI-based perspectives echo similar theoretical and methodological currents in traditional approaches while also offering new directions for research. The volume showcases examples from a wide range of texts, including novels, television shows, and films to illustrate the ways in which the latest AI technologies can be used for "dialoguing" with textual characters and examining textual meaning coherence. Illuminating the potential of AI language models to both enhance and extend research on the interpretation of texts, this book will appeal to scholars interested in cognitive approaches to the humanities in such fields as literary studies, discourse analysis, media studies, film studies, psychology, and artificial intelligence.

This book is the first of its kind in creating a snapshot of the state of the art in the use of technology in translating creative texts. The book gives an overview of a wide range of subjects that are developing rapidly and are likely to become substantially more important to both researchers and practitioners over the next few years. It includes work by researchers at all career stages, with strong representation by early and mid-career researchers who are likely to go on to shape the field in the coming years. It addresses active debates in the field (i.e. whether technology can/should be used in the translation of creative texts) from the perspective of data, rather than conjecture. Events and publications in the same field are increasing in number rapidly, along with the number of researchers expressing an interest in the topic. Therefore, this book is well placed to become influential in this development.

Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.

A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.

Empirical translation studies is a rapidly evolving research area. This volume, written by world-leading researchers, demonstrates the integration of two new research paradigms: socially-oriented and data driven approaches to empirical translation studies. These two models expand current translation studies and stimulate reader debates around how development of quantitative research methods and integration with advances in translation technologies would significantly increase the research capacities of translation studies. Highly engaging, the volume pioneers the development of socially-oriented innovative research methods to enhance the current research capacities of theoretical (descriptive) translation studies in order to tackle real-life research issues, such as environmental protection and multicultural health promotion. Illustrative case studies are used, bringing insight into advanced research methodologies of designing, developing and analysing large scale digital databases for multilingual and/or translation research.

Computational semantics is the art and science of computing meaning in natural language. The meaning of a sentence is derived from the meanings of the individual words in it, and this process can be made so precise that it can be implemented on a computer. Designed for students of linguistics, computer science, logic and philosophy, this comprehensive text shows how to compute meaning using the functional programming language Haskell. It deals with both denotational meaning (where meaning comes from knowing the conditions of truth in situations), and operational meaning (where meaning is an instruction for performing cognitive action). Including a discussion of recent developments in logic, it will be invaluable to linguistics students wanting to apply logic to their studies, logic students wishing to learn how their subject can be applied to linguistics, and functional programmers interested in natural language processing as a new application area.

Posthumanism and Deconstructing Arguments: Corpora and Digitally-driven Critical Analysis presents a new and practical approach in Critical Discourse Studies. Providing a data-driven and ethically-based method for the examination of arguments in the public sphere, this ground-breaking book: Highlights how the reader can evaluate arguments from points of view other than their own; Demonstrates how digital tools can be used to generate 'ethical subjectivities' from large numbers of dissenting voices on the world-wide-web; Draws on ideas from posthumanist philosophy as well as from Jacques Derrida, Gilles Deleuze and Felix Guattari for theorising these subjectivities; Showcases a critical deconstructive approach, using different corpus linguistic programs such as AntConc, WMatrix and Sketchengine. Posthumanism and Deconstructing Arguments is essential reading for lecturers and researchers with an interest in critical discourse studies, critical thinking, corpus linguistics and digital humanities.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.

Addresses a central problem in cognitive science, concerning the learning procedures through which humans acquire and represent natural language. Brings together world leading scholars from a range of disciplines, includingcomputational linguistics, psychology, behavioural science, and mathematical linguistics. Will appeal to researchers in computational and mathematical linguistics, psychology and behavioral science, AI and NLP. Represents a wide spectrum of perspectives

Innovative examination of augmentation technologies in terms of technical, social, and ethical considerations Usable as a supplemental text for a variety of courses, and also of interest to researchers and professionals in fields including: technical communication, digital communication, UX design, information technology, informatics, human factors, artificial intelligence, ethics, philosophy of technology, and sociology of technology First major work to combine technological, ethical, social, and rhetorical perspectives on human augmentation Additional cases and research material available at the authors' Fabric of Digital Life research database at https://fabricofdigitallife.com/

Recent developments in artificial intelligence, especially neural network and deep learning technology, have led to rapidly improving performance in voice assistants such as Siri and Alexa. Over the next few years, capability will continue to improve and become increasingly personalised. Today's voice assistants will evolve into virtual personal assistants firmly embedded within our everyday lives. Told through the view of a fictitious personal assistant called Cyba, this book provides an accessible but detailed overview of how a conversational voice assistant works, especially how it understands spoken language, manages conversations, answers questions and generates responses. Cyba explains through examples and diagrams the neural network technology underlying speech recognition and synthesis, natural language understanding, knowledge representation, conversation management, language translation and chatbot technology. Cyba also explores the implications of this rapidly evolving technology for security, privacy and bias, and gives a glimpse of future developments. Cyba's website can be found at HeyCyba.com.

The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks, cloud-based systems (e.g., Salesforce), databases (e.g., Oracle), AWS machine learning (e.g., Amazon Lex), and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our history. AI will be used from the C-suite to the distribution warehouse floor. Replete with case studies, this book provides a working knowledge of AI's current and future capabilities and the impact it will have on every business. It covers everything from healthcare to warehousing, banking, finance and education. It is essential reading for anyone involved in industry.

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offers self-contained coverage of semisupervised methods that includes background material on supervised and unsupervised learning. The book presents a brief history of semisupervised learning and its place in the spectrum of learning methods before moving on to discuss well-known natural language processing methods, such as self-training and co-training. It then centers on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, support vector machines (SVMs), and the null-category noise model. In addition, the book covers clustering, the expectation-maximization (EM) algorithm, related generative methods, and agreement methods. It concludes with the graph-based method of label propagation as well as a detailed discussion of spectral methods. Taking an intuitive approach to the material, this lucid book facilitates the application of semisupervised learning methods to natural language processing and provides the framework and motivation for a more systematic study of machine learning.

The use of literature in second language teaching has been advocated for a number of years, yet despite this there have only been a limited number of studies which have sought to investigate its effects. Fewer still have focused on its potential effects as a model of spoken language or as a vehicle to develop speaking skills. Drawing upon multiple research studies, this volume fills that gap to explore how literature is used to develop speaking skills in second language learners. The volume is divided into two sections: literature and spoken language and literature and speaking skills. The first section focuses on studies exploring the use of literature to raise awareness of spoken language features, whilst the second investigates its potential as a vehicle to develop speaking skills. Each section contains studies with different designs and in various contexts including China, Japan and the UK. The research designs used mean that the chapters contain clear implications for classroom pedagogy and research in different contexts.

Contemporary corpus linguists use a wide variety of methods to study discourse patterns. This volume provides a systematic comparison of various methodological approaches in corpus linguistics through a series of parallel empirical studies that use a single corpus dataset to answer the same overarching research question. Ten contributing experts each use a different method to address the same broadly framed research question: In what ways does language use in online Q+A forum responses differ across four world English varieties (India, Philippines, United Kingdom, and United States)? Contributions will be based on analysis of the same 400,000 word corpus from online Q+A forums, and contributors employ methodologies including corpus-based discourse analysis, audience perceptions, Multi-Dimensional analysis, pragmatic analysis, and keyword analysis. In their introductory and concluding chapters, the volume editors compare and contrast the findings from each method and assess the degree to which 'triangulating' multiple approaches may provide a more nuanced understanding of a research question, with the aim of identifying a set of complementary approaches which could arguably take into account analytical blind spots. Baker and Egbert also consider the importance of issues such as researcher subjectivity, type of annotation, the limitations and affordances of different corpus tools, the relative strengths of qualitative and quantitative approaches, and the value of considering data or information beyond the corpus. Rather than attempting to find the 'best' approach, the focus of the volume is on how different corpus linguistic methodologies may complement one another, and raises suggestions for further methodological studies which use triangulation to enrich corpus-related research.