Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book DescriptionNLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is forProgrammers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

'A must-read' New Scientist 'Fascinating' Greta Thunberg 'Enthralling' George Monbiot 'Brilliant' Philip Hoare A thrilling investigation into the pioneering world of animal communication, where big data and artificial intelligence are changing our relationship with animals forever In 2015, wildlife filmmaker Tom Mustill was whale watching when a humpback breached onto his kayak and nearly killed him. After a video clip of the event went viral, Tom found himself inundated with theories about what happened. He became obsessed with trying to find out what the whale had been thinking and sometimes wished he could just ask it. In the process of making a film about his experience, he discovered that might not be such a crazy idea. This is a story about the pioneers in a new age of discovery, whose cutting-edge developments in natural science and technology are taking us to the brink of decoding animal communication - and whales, with their giant mammalian brains and sophisticated vocalisations, offer one of the most realistic opportunities for us to do so. Using 'underwater ears,' robotic fish, big data and machine intelligence, leading scientists and tech-entrepreneurs across the world are working to turn the fantasy of Dr Dolittle into a reality, upending much of what we know about these mysterious creatures. But what would it mean if we were to make contact? And with climate change threatening ever more species with extinction, would doing so alter our approach to the natural world? Enormously original and hugely entertaining, How to Speak Whale is an unforgettable look at how close we truly are to communicating with another species - and how doing so might change our world beyond recognition.

"Exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it." -John Horgan "If you want to know about AI, read this book...It shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence." -Peter Thiel Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. A computer scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to reveal why this is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets. We make conjectures, informed by context and experience. And we haven't a clue how to program that kind of intuitive reasoning, which lies at the heart of common sense. Futurists insist AI will soon eclipse the capacities of the most gifted mind, but Larson shows how far we are from superintelligence-and what it would take to get there. "Larson worries that we're making two mistakes at once, defining human intelligence down while overestimating what AI is likely to achieve...Another concern is learned passivity: our tendency to assume that AI will solve problems and our failure, as a result, to cultivate human ingenuity." -David A. Shaywitz, Wall Street Journal "A convincing case that artificial general intelligence-machine-based intelligence that matches our own-is beyond the capacity of algorithmic machine learning because there is a mismatch between how humans and machines know what they know." -Sue Halpern, New York Review of Books

Optimize 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.

Studies in Computational Linguistics presents authoritative texts from an international team of leading computational linguists. The books range from the senior undergraduate textbook to the research level monograph and provide a showcase for a broad range of recent developments in the field. The series should be interesting reading for researchers and students alike involved at this interface of linguistics and computing.

One of the liveliest forums for sharing psychological, linguistic, philosophical, and computer science perspectives on psycholinguistics has been the annual meeting of the CUNY Sentence Processing Conference. Documenting the state of the art in several important approaches to sentence processing, this volume consists of selected papers that had been presented at the Sixth CUNY Conference. The editors not only present the main themes that ran through the conference but also honor the breadth of the presentations from disciplines including linguistics, experimental psychology, and computer science. The variety of sentence processing topics examined includes:
* how evoked brain potentials reflect sentence comprehension
* how auditory words are processed
* how various sources of grammatical and nongrammatical information are coordinated and used
* how sentence processing and language acquisition might be related.
This distinctive volume not only presents the most exciting current work in sentence processing, but also places this research into the broader context of theorizing about it.

This edited volume covers the development and application of metalanguages for concretely describing and communicating translation processes in practice. In a modern setting of project-based translation, it is crucial to bridge the gaps between various actors involved in the translation process, especially among clients, translation service providers (TSPs), translators, and technology developers. However, we have been confronted with the lack of common understanding among them about the notion and detailed mechanisms of translation. Against this backdrop, we are developing systematic, fine-grained metalanguages that are designed to describe and analyse translation processes in concrete terms. Underpinned by the rich accumulation of theoretical findings in translation studies and established standards of practical translation services, such as ISO 17100, our metalanguages extensively cover the core processes in translation projects, namely project management, source document analysis, translation, and revision. Gathering authors with diverse backgrounds and expertise, this book proffers the fruits of the contributors' collaborative endeavour; it not only provides practicable metalanguages, but also reports on wide-ranging case studies on the application of metalanguages in practical and pedagogical scenarios. This book supplies concrete guidance for those who are involved in the translation practices and translation training/education. In addition to being of practical use, the metalanguages reflect explication of the translation process. As such, this book provides essential insights for researchers and students in the field of translation studies.

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.

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

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

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.

This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.

Natural language understanding is central to the goals of artificial intelligence. Any truly intelligent machine must be capable of carrying on a conversation: dialogue, particularly clarification dialogue, is essential if we are to avoid disasters caused by the misunderstanding of the intelligent interactive systems of the future. This book is an interim report on the grand enterprise of devising a machine that can use natural language as fluently as a human. What has really been achieved since this goal was first formulated in Turing's famous test? What obstacles still need to be overcome?

TensorFlow is a one-stop solution for building, monitoring, optimizing,and deploying your models. This practical guide to building deep learning models with the new features of TensorFlow 2.0is filled with engaging projects, simple language, and coverage of the latest algorithms. TensorFlow 2.0 in Action teaches you to use the new features of TensorFlow 2.0 to create advanced deep learning models. You'll learn by building hands-on projects including an image classifier that can recognize objects, a French-to-English machine translator, and even a neural network that can write fiction. You'll dive into the details of modern deep learning techniques including both transformer and attention models, and learn how pretrained models can solve your tricky data science- problems. TensorFlow is the go-to framework for putting deep learning into production. Created by Google, this ground breaking tool handles repetitive low-level operations and frees you up to focus on innovating your AIs.TensorFlow encompasses almost every element of a deep learning pipeline-aone-stop solution for building, monitoring, optimizing, and deploying your models.

In the not so distant future, we can expect a world where humans and robots coexist and interact with each other. For this to occur, we need to understand human traits, such as seeing, hearing, thinking, speaking, etc., and institute these traits in robots. The most essential feature necessary for robots to achieve is that of integrative multimedia understanding (IMU) which occurs naturally in humans. It allows us to assimilate pieces of information expressed through different modes such as speech, pictures, gestures, etc. The book describes how robots acquire traits like natural language understanding (NLU) as the central part of IMU. Mental image directed semantic theory (MIDST) is its core, and is based on the hypothesis that NLU is essentially the processing of mental image associated with natural language expressions, namely, mental-image based understanding (MBU). MIDST is intended to model omnisensory mental image in human and to afford a knowledge representation system in order for integrative management of knowledge subjective to cognitive mechanisms of intelligent entities such as humans and robots based on a mental image model visualized as 'Loci in Attribute Spaces' and its description language Lmd (mental image description language) to be employed for predicate logic with a systematic scheme for symbol-grounding. This language works as an interlingua among various kinds of information media, and has been applied to several versions of the intelligent system interlingual understanding model aiming at general system (IMAGES). Its latest version, i.e. conversation management system (CMS) simulates MBU and comprehends the user's intention through dialogue to find and solve problems, and finally, provides a response in text or animation. The book is aimed at researchers and students interested in artificial intelligence, robotics, and cognitive science. Based on philosophical considerations, the methodology will also have an appeal in linguistics, psychology, ontology, geography, and cartography. Key Features: Describes the methodology to provide robots with human-like capability of natural language understanding (NLU) as the central part of IMU Uses methodology that also relates to linguistics, psychology, ontology, geography, and cartography Examines current trends in machine translation

This text 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.

Natural language understanding is central to the goals of artificial intelligence. Any truly intelligent machine must be capable of carrying on a conversation: dialogue, particularly clarification dialogue, is essential if we are to avoid disasters caused by the misunderstanding of the intelligent interactive systems of the future. This book is an interim report on the grand enterprise of devising a machine that can use natural language as fluently as a human. What has really been achieved since this goal was first formulated in Turing's famous test? What obstacles still need to be overcome?

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.

New to the Second Edition

• Greater prominence of statistical approaches
• New applications section
• Broader multilingual scope to include Asian and European languages, along with English
• An actively maintained wiki (http: //handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developments

Divided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.

Research into Natural Language Processing - the use of computers to process language - has developed over the last couple of decades into one of the most vigorous and interesting areas of current work on language and communication. This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques in the field. The programming language used is Prolog, which is especially well-suited for Natural Language Processing and those with little or no background in computing. Following the general introduction, the first section of the book presents Prolog, and the following chapters illustrate how various Natural Language Processing programs may be written using this programming language. Since it is assumed that the reader has no previous experience in programming, great care is taken to provide a simple yet comprehensive introduction to Prolog. Due to the 'user friendly' nature of Prolog, simple yet effective programs may be written from an early stage. The reader is gradually introduced to various techniques for syntactic processing, ranging from Finite State Network recognisors to Chart parsers. An integral element of the book is the comprehensive set of exercises included in each chapter as a means of cementing the reader's understanding of each topic. Suggested answers are also provided. An Introduction to Natural Language Processing Through Prolog is an excellent introduction to the subject for students of linguistics and computer science, and will be especially useful for those with no background in the subject.

Research into Natural Language Processing - the use of computers to process language - has developed over the last couple of decades into one of the most vigorous and interesting areas of current work on language and communication. This book introduces the subject through the discussion and development of various computer programs which illustrate some of the basic concepts and techniques in the field. The programming language used is Prolog, which is especially well-suited for Natural Language Processing and those with little or no background in computing. Following the general introduction, the first section of the book presents Prolog, and the following chapters illustrate how various Natural Language Processing programs may be written using this programming language. Since it is assumed that the reader has no previous experience in programming, great care is taken to provide a simple yet comprehensive introduction to Prolog. Due to the 'user friendly' nature of Prolog, simple yet effective programs may be written from an early stage. The reader is gradually introduced to various techniques for syntactic processing, ranging from Finite State Network recognisors to Chart parsers. An integral element of the book is the comprehensive set of exercises included in each chapter as a means of cementing the reader's understanding of each topic. Suggested answers are also provided. An Introduction to Natural Language Processing Through Prolog is an excellent introduction to the subject for students of linguistics and computer science, and will be especially useful for those with no background in the subject.

This 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

The techniques of natural language processing (NLP) have been widely applied in machine translation and automated message understanding, but have only recently been utilized in second language teaching. This book offers both an argument for and a critical examination of this new application, with an examination of how systems may be designed to exploit the power of NLP, accomodate its limitations, and minimize its risks. This volume marks the first collection of work in the U.S. and Canada that incorporates advanced human language technologies into language tutoring systems, covering languages as diverse as Arabic, Spanish, Japanese, and English.
The book is organized into sections that express the levels of analysis dealt with in learning and teaching a language and with the tasks of the student as writer, reader, conversant, and actor in the world. These sections bring together research by specialists in linguistics, artificial intelligence, psychology, instructional design, and language teaching. In addition to providing detailed descriptions of working systems, amply illustrated with screens from lesson and authoring interfaces, the contributors address a spectrum of common issues:
* What can current NLP technology contribute to computer-assisted language instruction and to research on language learning?
* How can this technology meet the demands of pedagogical theory for communicative language teaching in authentic contexts?
* How can designers constrain tutoring environments to ensure accurate analysis of learners' language?
* What can NLP-based systems teach us about language acquisition, about linguistic theory, and about theories of language pedagogy?
* What lessons have been learned in using these systems to date?
Discipline-specific issues are illuminated as well: the relative merits of the major syntactic frameworks for NLP-based language tutoring; the adaptation of theories like lexical conceptual structure to support semantic interpretation; the integration of input language with visual microworlds and dialogue games; the pragmatics of the tutoring discourse; the selection of instructional principles to guide system design; and the accomodation of design to individual differences and learner styles. A concluding section assesses this work from larger theoretical and practical perspectives -- experimental psychology and psycholinguistics, linguistics, language teaching, and second language acquisition research.

Originally published in 1992, when connectionist natural language processing (CNLP) was a new and burgeoning research area, this book represented a timely assessment of the state of the art in the field. It includes contributions from some of the best known researchers in CNLP and covers a wide range of topics. The book comprises four main sections dealing with connectionist approaches to semantics, syntax, the debate on representational adequacy, and connectionist models of psycholinguistic processes. The semantics and syntax sections deal with a variety of approaches to issues in these traditional linguistic domains, covering the spectrum from pure connectionist approaches to hybrid models employing a mixture of connectionist and classical AI techniques. The debate on the fundamental suitability of connectionist architectures for dealing with natural language processing is the focus of the section on representational adequacy. The chapters in this section represent a range of positions on the issue, from the view that connectionist models are intrinsically unsuitable for all but the associationistic aspects of natural language, to the other extreme which holds that the classical conception of representation can be dispensed with altogether. The final section of the book focuses on the application of connectionist models to the study of psycholinguistic processes. This section is perhaps the most varied, covering topics from speech perception and speech production, to attentional deficits in reading. An introduction is provided at the beginning of each section which highlights the main issues relating to the section topic and puts the constituent chapters into a wider context.

The techniques of natural language processing (NLP) have been widely applied in machine translation and automated message understanding, but have only recently been utilized in second language teaching. This book offers both an argument for and a critical examination of this new application, with an examination of how systems may be designed to exploit the power of NLP, accomodate its limitations, and minimize its risks. This volume marks the first collection of work in the U.S. and Canada that incorporates advanced human language technologies into language tutoring systems, covering languages as diverse as Arabic, Spanish, Japanese, and English.
The book is organized into sections that express the levels of analysis dealt with in learning and teaching a language and with the tasks of the student as writer, reader, conversant, and actor in the world. These sections bring together research by specialists in linguistics, artificial intelligence, psychology, instructional design, and language teaching. In addition to providing detailed descriptions of working systems, amply illustrated with screens from lesson and authoring interfaces, the contributors address a spectrum of common issues:
* What can current NLP technology contribute to computer-assisted language instruction and to research on language learning?
* How can this technology meet the demands of pedagogical theory for communicative language teaching in authentic contexts?
* How can designers constrain tutoring environments to ensure accurate analysis of learners' language?
* What can NLP-based systems teach us about language acquisition, about linguistic theory, and about theories of language pedagogy?
* What lessons have been learned in using these systems to date?
Discipline-specific issues are illuminated as well: the relative merits of the major syntactic frameworks for NLP-based language tutoring; the adaptation of theories like lexical conceptual structure to support semantic interpretation; the integration of input language with visual microworlds and dialogue games; the pragmatics of the tutoring discourse; the selection of instructional principles to guide system design; and the accomodation of design to individual differences and learner styles. A concluding section assesses this work from larger theoretical and practical perspectives -- experimental psychology and psycholinguistics, linguistics, language teaching, and second language acquisition research.