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Deep Learning for Natural Language Processing
Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context.Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively.In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning * Models for textual similarity * Deep memory-based NLP * Semantic role labeling * Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP.No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language.Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics.He's the technical coordinator of two large European Union-funded research security-related projects.He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.
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Digital Teaching and Learning in Higher Education : Culture, Language, Social Issues
Digital collaboration has been established in higher education for many years.But when the Covid-19 pandemic struck, digital learning and virtual mobility became of utmost importance for higher education. In the international project »Digital and International Virtual Academic Cooperation« (DIVA), scholars from Israel, Australia, and Germany focused on intercultural learning and online collaboration.Based on their findings, they show how digital arrangements can be used in higher education, how digital teaching can be theorized, and what potential can be gained for post-pandemic teaching.
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Machine Learning and Deep Learning in Natural Language Processing
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken.NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results.Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field. NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal.This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.
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Natural Language Processing : A Machine Learning Perspective
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.
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How can I motivate myself to learn natural sciences, history, and culture?
To motivate yourself to learn natural sciences, history, and culture, you can start by setting specific and achievable goals for each subject. Break down the topics into smaller, manageable chunks and reward yourself when you reach each milestone. Additionally, find ways to make the learning process enjoyable, such as watching documentaries, visiting museums, or participating in hands-on experiments. Surrounding yourself with like-minded individuals who share your interests can also provide motivation and support. Finally, remind yourself of the value of gaining knowledge in these areas and how it can contribute to your personal and intellectual growth.
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What is the focus in the upper school, language or natural sciences?
In the upper school, the focus is typically on both language and natural sciences. Students are often required to take a variety of courses in both areas in order to provide a well-rounded education. This allows students to develop skills in critical thinking, problem-solving, and communication, while also gaining an understanding of the natural world and scientific principles. Ultimately, the focus in the upper school is to provide a comprehensive education that prepares students for future academic and career pursuits in a variety of fields.
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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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Graph Learning and Network Science for Natural Language Processing
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 NLPCovers recent computational intelligence techniques for graph-based neural network modelsDiscusses advances in random walk-based techniques, semantic webs, and lexical networksExplores recent research into NLP for graph-based streaming dataReviews advances in knowledge graph embedding and ontologies for NLP approachesThis book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.
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Deep Learning for Natural Language Processing : A Gentle Introduction
Deep Learning is becoming increasingly important in a technology-dominated world.However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics.This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing.It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way.It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face.Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.
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Natural Language Processing with PyTorchlow : Build Intelligent Language Applications Using Deep Learning
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible.If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face.Each chapter includes several code examples and illustrations.Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
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Wired for Culture : The Natural History of Human Cooperation
'Expresses an infectious sense of wonder at the uniqueness of our species; it is hard not to be affected by his enthusiasm' Sunday TimesWhat explains the staggering diversity of cultures in the world?Why are there so many languages, even within small areas?Why do we rejoice in rituals and wrap ourselves in flags?In Wired for Culture Mark Pagel, the world's leading expert on human development, reveals how our facility for culture is the key to what makes us who we are. Shedding light on everything from art, morality and affection to jealousy, self-interest and prejudice, Pagel shows that we developed culture - cooperating together and passing on knowledge - in order to survive.Our minds are hardwired for culture, and it still determines how we speak, who we love, why we kill and what we think today. 'Human evolution may be the hottest area in popular science writing.Within this field, Wired for Culture stands out for both its sweeping erudition and its accessibility ... richly rewarding' Financial Times 'Impressive for its detail, accuracy and vivacity' Guardian 'Pioneering, vivid ... the best popular science book on culture so far' Nature
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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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Are nations a quasi-natural unit of European history?
Nations are not a quasi-natural unit of European history, as they are socially constructed entities that have evolved over time through various political, cultural, and historical processes. The concept of nations as we know them today is a relatively modern phenomenon, emerging in the 19th and 20th centuries with the rise of nationalism. European history is characterized by a complex interplay of various ethnic, linguistic, and cultural groups, and the boundaries of nations have often been contested and redrawn throughout history. Therefore, nations should be seen as a fluid and dynamic aspect of European history rather than a fixed or natural unit.
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What are natural or near-natural ecosystems?
Natural or near-natural ecosystems are areas of land or water that have not been significantly altered by human activity. These ecosystems are characterized by a high degree of biodiversity and are home to a wide variety of plant and animal species. They are often self-sustaining and have complex interactions between different organisms and their environment. Examples of natural or near-natural ecosystems include old-growth forests, coral reefs, and wetlands. These ecosystems are important for maintaining ecological balance and providing essential services such as clean water, air, and habitat for wildlife.
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Is natural science difficult in high school?
Natural science can be challenging in high school, as it often involves complex concepts and requires a strong understanding of scientific principles. Students may find it difficult to grasp certain topics, such as physics or chemistry, and may struggle with the mathematical aspects of these subjects. However, with dedication and effective study habits, many students are able to succeed in natural science courses and even develop a passion for the subject. It's important for students to seek help from teachers or tutors if they are struggling, and to approach the material with a positive attitude and willingness to learn.
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