Products related to Natural:
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Pop Culture in Language Education : Theory, Research, Practice
Pop Culture in Language Education provides comprehensive insight on how studies of pop culture can inform language teaching and learning.The volume offers a state-of-the-art overview of empirically informed, cutting-edge research that tackles both theoretical concerns and practical implications.The book focuses on how a diverse array of pop culture artifacts such as pop and rap music, movies and TV series, comics and cartoons, fan fiction, and video games can be exploited for the development of language skills.It establishes the study of pop culture and its language as a serious subfield within language education and applied linguistics and explores how studies of pop culture, its language, and its non-linguistic affordances can inform language education at various levels of proficiency and with various learner populations. Presenting a broad range of quantitative and qualitative research approaches including case studies on how pop culture has been used successfully in language education in and beyond the classroom, this book will be of great interest for academics, researchers, and students in the field of language education, applied linguistics, psycholinguistics, and sociolinguistics, as well as for language teachers and materials developers.
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Natural Language Processing with Python : Analyzing Text with the Natural Language Tool Kit
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.With it, you'll learn how to write Python programs that work with large collections of unstructured text.You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: * Extract information from unstructured text, either to guess the topic or identify "named entities" * Analyze linguistic structure in text, including parsing and semantic analysis * Access popular linguistic databases, including WordNet and treebanks * Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
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Handbook of Natural Language Processing
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 approachesNew applications sectionBroader multilingual scope to include Asian and European languages, along with EnglishAn actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developmentsDivided 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.
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Introduction to Natural Language Processing
A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing-methods for building computer software that understands, generates, and manipulates human language.It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning.The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis.The second section introduces structured representations of language, including sequences, trees, and graphs.The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings.The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation.End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations.It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists.Readers should have a background in computer programming and college-level mathematics.After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
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What is Natural Science and Technology (NWT)?
Natural Science and Technology (NWT) is an interdisciplinary subject that combines elements of natural sciences, technology, and engineering. It focuses on understanding the natural world, how technology impacts our lives, and how we can use scientific knowledge to solve real-world problems. NWT encourages students to explore the connections between science and technology and how they influence each other in our daily lives. Through hands-on experiments, research projects, and critical thinking activities, students develop a deeper understanding of the world around them and the role of science and technology in shaping our future.
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How are natural science and technology related?
Natural science and technology are closely related as they both involve the study and understanding of the natural world. Natural science seeks to understand the fundamental principles and laws that govern the natural world, while technology applies this understanding to create practical solutions to real-world problems. Technology often builds upon the knowledge gained from natural science to develop new tools, materials, and processes that improve our lives and our understanding of the natural world. In this way, natural science and technology work hand in hand to advance our understanding and utilization of the natural world.
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Can one fail in natural sciences and technology?
Yes, it is possible to fail in natural sciences and technology. Just like any other subject, success in these fields requires dedication, hard work, and a solid understanding of the material. Failing in natural sciences and technology could be due to a lack of understanding of complex concepts, insufficient study habits, or a lack of interest in the subject matter. However, failure does not necessarily mean that a person cannot succeed in these fields in the future with the right support and effort.
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Is Germany too focused on technology and natural sciences?
While Germany has a strong emphasis on technology and natural sciences, it also values other fields such as arts, humanities, and social sciences. The country's education system and job market do prioritize STEM fields, but there are still opportunities for individuals in other disciplines. Germany's focus on technology and natural sciences has contributed to its economic success, but it is important to maintain a balance and support a diverse range of disciplines for a well-rounded society.
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Natural Language Processing : Semantic Aspects
This 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.
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Real-World Natural Language Processing
Voice 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 applicationsNLP libraries such as AllenNLP and FairseqAdvanced 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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Natural Language Processing in Action
Develop your NLP skills from scratch! This revised bestseller now includes coverage of the latest Python packages, Transformers, the HuggingFace packages, and chatbot frameworks. In Natural Language Processing in Action, Second Edition you will learn how to: Process, analyze, understand, and generate natural language textBuild production-quality NLP pipelines with spaCyBuild neural networks for NLP using PytorchBERT and GPT transformers for English composition, writing code, and even organizing your thoughtsCreate chatbots and other conversational AI agents Natural Language Processing in Action has helped thousands of data scientists build machines that understand human language.In this new and revised edition, you'll discover state-of-the art NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more.As you go, you'll create projects that can detect fake news, filter spam, and even answer your questions, all built with Python and its ecosystem of data tools. about the technology Deep learning has been a giant leap forward for NLP applications.Developers can now build chatbots and other natural language tools that can imitate real people, diagnose illness, automatically summarize documents, and more.Accessible open source tools such as spaCy and PyTorch make production-level NLP easier and more impactful than ever before. about the book Natural Language Processing in Action, Second Edition is your guide to building software that can read and interpret human language.This new edition is updated to include the latest Python packages and comes with full coverage of cutting-edge models like BERT, GPT-J and HuggingFace transformers. In it, you'll learn to create fun and useful NLP applications such as semantic search engines that are even better than Google, chatbots that can help you write a book, and a multilingual translation program.Soon, you'll be ready to start tackling real-world problems with NLP. AUTHOR BIO Hobson Lane is a data scientist and machine learning engineer.He has over twenty years experience building autonomous systems and NLP pipelines for both large corporations and start-ups.Currently, Hobson is an instructor at UCSD Extension and Springboard, and the CTO and cofounder of Tangible AI and ProAI.org. Maria Dyshel is a social entrepreneur and artificial intelligence expert.She held a variety of AI research, engineering, and management roles in diverse industries, from designing and improving algorithms for autonomous vehicles, to implementing company-wide Conversational AI program in one of the world's largest pharma companies.Maria is currently the CEO and cofounder of Tangible AI. Cole Howard and Hannes Max Hapke were authors of the first edition of Natural Language Processing in Action and their legacy lives on in the collective intelligence of the community around the book.
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Language Intelligence : Expanding Frontiers in Natural Language Processing
Thorough review of foundational concepts and advanced techniques in natural language processing (NLP) and its impact across sectors Supported by examples and case studies throughout, Language Intelligence provides an in-depth exploration of the latest advancements in natural language processing (NLP), offering a unique blend of insight on theoretical foundations, practical applications, and future directions in the field.Composed of 10 chapters, this book provides a thorough understanding of both foundational concepts and advanced techniques, starting with an overview of the historical development of NLP and essential mechanisms of Natural Language Understanding (NLU) and Natural Language Generation (NLG).It delves into the data landscape crucial for NLP, emphasizing ethical considerations, and equips readers with fundamental text processing techniques.The book also discusses linguistic features central to NLP and explores computational and cognitive approaches that enrich the field's advancement.Practical applications and advanced processing techniques across various sectors like healthcare, legal, finance, and education are showcased, along with a critical examination of NLP metrics and methods for evaluation.The appendices offer detailed explorations of text representation methods, advanced applications, and Python's NLP capabilities, aiming to inform, inspire, and ignite a passion for NLP in the ever-expanding digital universe.Written by a highly qualified academic with significant research experience in the field, Language Intelligence covers sample topics including: Fundamental text processing, covering text cleaning, sentence splitting, tokenization, lemmatization and stemming, stop-word removal, part-of-speech tagging, and parsing and syntactic analysisComputational and cognitive approaches, covering human-like reasoning, transfer learning, and learning with minimal examplesAffective, psychological, and content analysis, covering sentiment analysis, emotion recognition, irony, humour, and sarcasm detection, and indicators of distressMultilingual natural language processing, covering translation and transliteration, cross-lingual models and embeddings, low-resource language processing, and cultural nuance and idiom recognition Language Intelligence is an ideal reference on the subject for professionals across sectors and graduate students in related programs of study who have a foundational understanding of computer science, linguistics, and artificial intelligence and are looking to delve deeper into the intricacies of NLP.
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What is the relationship between natural sciences and technology?
The natural sciences and technology are closely related fields that often work hand in hand. Natural sciences, such as biology, chemistry, and physics, provide the foundational knowledge and understanding of the natural world, which is then applied in the development of technology. Technology, on the other hand, utilizes the principles and discoveries of natural sciences to create new tools, products, and processes that improve our lives and advance our understanding of the world. In this way, natural sciences provide the knowledge base for technology, while technology applies that knowledge to create practical solutions and innovations.
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How can one improve Natural Language Understanding (NC)?
One can improve Natural Language Understanding (NLU) by utilizing advanced machine learning and deep learning techniques to train models on large and diverse datasets. This can help the models to better understand the nuances and complexities of natural language. Additionally, incorporating contextual information and domain-specific knowledge can enhance NLU by enabling the models to better comprehend the meaning and intent behind the language. Furthermore, continuous evaluation and refinement of NLU models based on real-world feedback and usage can also contribute to improvement in NLU.
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'How do you pronounce it in natural language?'
In natural language, "pronounce" is typically pronounced as "pruh-nouns." The emphasis is on the first syllable, and the "ou" sound is pronounced like the "ow" in "cow." The "nounce" part is pronounced like "ounce" without the "o" sound at the beginning.
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Which branch, natural sciences or language, is easier?
The difficulty of a branch of study, whether natural sciences or language, is subjective and depends on an individual's strengths and interests. Some people may find natural sciences easier due to their analytical and logical thinking skills, while others may find language easier due to their creativity and communication abilities. Both branches require dedication and hard work to excel in, and the perceived difficulty may vary from person to person. Ultimately, the ease of a branch of study depends on the individual's aptitude and passion for the subject.
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