Natural Language Processing In Artificial Intelligence Nlpinai 2020
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Natural Language Processing in Artificial Intelligence—NLPinAI 2020 by Roussanka Loukanova Pdf
This book covers theoretical work, applications, approaches, and techniques for computational models of information and its presentation by language (artificial, human, or natural in other ways). Computational and technological developments that incorporate natural language are proliferating. Adequate coverage encounters difficult problems related to ambiguities and dependency on context and agents (humans or computational systems). The goal is to promote computational systems of intelligent natural language processing and related models of computation, language, thought, mental states, reasoning, and other cognitive processes.
Natural Language Processing in Artificial Intelligence — NLPinAI 2021 by Roussanka Loukanova Pdf
The book covers theoretical work, approaches, applications, and techniques for computational models of information, language, and reasoning. Computational and technological developments that incorporate natural language are proliferating. Adequate coverage of natural language processing in artificial intelligence encounters problems on developments of specialized computational approaches and algorithms. Many difficulties are due to ambiguities in natural language and dependency of interpretations on contexts and agents. Classical approaches proceed with relevant updates, and new developments emerge in theories of formal and natural languages, computational models of information and reasoning, and related computerized applications. Its focus is on computational processing of human language and relevant medium languages, which can be theoretically formal, or for programming and specification of computational systems. The goal is to promote intelligent natural language processing, along with models of computation, language, reasoning, and other cognitive processes.
Natural Language Processing in Artificial Intelligence - NLPinAI 2021 by Roussanka Loukanova Pdf
The book covers theoretical work, approaches, applications, and techniques for computational models of information, language, and reasoning. Computational and technological developments that incorporate natural language are proliferating. Adequate coverage of natural language processing in artificial intelligence encounters problems on developments of specialized computational approaches and algorithms. Many difficulties are due to ambiguities in natural language and dependency of interpretations on contexts and agents. Classical approaches proceed with relevant updates, and new developments emerge in theories of formal and natural languages, computational models of information and reasoning, and related computerized applications. Its focus is on computational processing of human language and relevant medium languages, which can be theoretically formal, or for programming and specification of computational systems. The goal is to promote intelligent natural language processing, along with models of computation, language, reasoning, and other cognitive processes.
Natural Language Processing in Artificial Intelligence by Brojo Kishore Mishra,Raghvendra Kumar Pdf
This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.
Deep Learning in Natural Language Processing by Li Deng,Yang Liu Pdf
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
Covers all aspects of the area of linguistic analysis and the computational systems that have been developed to perform the language analysis. The book is primarily meant for post graduate and undergraduate technical courses.
Handbook of Natural Language Processing by Robert Dale,Hermann Moisl,Harold Somers Pdf
This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.
Deep Learning for Natural Language Processing by Karthiek Reddy Bokka,Shubhangi Hora,Tanuj Jain,Monicah Wambugu Pdf
Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Author : Michael Richter Publisher : Walter de Gruyter GmbH & Co KG Page : 192 pages File Size : 51,5 Mb Release : 2024-02-19 Category : Language Arts & Disciplines ISBN : 9783110788143
Modelling Natural Language with Claude Shannon’s Notion of Surprisal by Michael Richter Pdf
Have you ever wondered how the principles behind Shannon's groundbreaking Information Theory can be interwoven with the intricate fabric of linguistic communication? This book takes you on a fascinating journey, offering insights into how humans process and comprehend language. By applying Information Theory to the realm of natural language semantics, it unravels the connection between regularities in linguistic messages and the cognitive intricacies of language processing. Highlighting the intersections of information theory with linguistics, philosophy, cognitive psychology, and computer science, this book serves as an inspiration for anyone seeking to understand the predictive capabilities of Information Theory in modeling human communication. It elaborates on the seminal works from giants in the field like Dretske, Hale, and Zipf, exploring concepts like surprisal theory and the principle of least effort. With its empirical approach, this book not only discusses the theoretical aspects but also ventures into the application of Shannon's Information Theory in real-world language scenarios, strengthened by advanced statistical methods and machine learning. It touches upon challenging areas such as the distinction between mathematical and semantic information, the concept of information in linguistic utterances, and the intricate play between truth, context, and meaning. Whether you are a linguist, a cognitive psychologist, a philosopher, or simply an enthusiast eager to dive deep into the world where language meets information, this book promises a thought-provoking journey.
Deep Natural Language Processing and AI Applications for Industry 5.0 by Tanwar, Poonam,Saxena, Arti,Priya, C. Pdf
To sustain and stay at the top of the market and give absolute comfort to the consumers, industries are using different strategies and technologies. Natural language processing (NLP) is a technology widely penetrating the market, irrespective of the industry and domains. It is extensively applied in businesses today, and it is the buzzword in every engineer’s life. NLP can be implemented in all those areas where artificial intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has improved the imitation of professional translations over the years. When applied in neural machine translation, NLP helps educate neural machine networks. This can be used by industries to translate low-impact content including emails, regulatory texts, etc. Such machine translation tools speed up communication with partners while enriching other business interactions. Deep Natural Language Processing and AI Applications for Industry 5.0 provides innovative research on the latest findings, ideas, and applications in fields of interest that fall under the scope of NLP including computational linguistics, deep NLP, web analysis, sentiments analysis for business, and industry perspective. This book covers a wide range of topics such as deep learning, deepfakes, text mining, blockchain technology, and more, making it a crucial text for anyone interested in NLP and artificial intelligence, including academicians, researchers, professionals, industry experts, business analysts, data scientists, data analysts, healthcare system designers, intelligent system designers, practitioners, and students.
Neural Networks for Natural Language Processing by S., Sumathi,M., Janani Pdf
Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.
Natural Language Processing with TensorFlow by Thushan Ganegedara Pdf
Write modern natural language processing applications using deep learning algorithms and TensorFlow Key Features Focuses on more efficient natural language processing using TensorFlow Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches Provides choices for how to process and evaluate large unstructured text datasets Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligence Book Description Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. What you will learn Core concepts of NLP and various approaches to natural language processing How to solve NLP tasks by applying TensorFlow functions to create neural networks Strategies to process large amounts of data into word representations that can be used by deep learning applications Techniques for performing sentence classification and language generation using CNNs and RNNs About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks How to write automatic translation programs and implement an actual neural machine translator from scratch The trends and innovations that are paving the future in NLP Who this book is for This book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.
The book, “Intelligent Computing - Proceedings of the 2022 Computing Conference”, is a comprehensive collection of chapters focusing on the core areas of computing and their further applications in the real world. Each chapter is a paper presented at the Computing Conference 2022 held on July 14–15, 2022. Computing 2022 attracted a total of 498 submissions which underwent a double-blind peer-review process. Of those 498 submissions, 179 submissions have been selected to be included in this book. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this book interesting and valuable as it provides the state-of-the-art intelligent methods and techniques for solving real-world problems. We also expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject.
Natural Language Processing with PyTorch by Delip Rao,Brian McMahan Pdf
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
Handbook of Research on Natural Language Processing and Smart Service Systems by Pazos-Rangel, Rodolfo Abraham,Florencia-Juarez, Rogelio,Paredes-Valverde, Mario Andrés,Rivera, Gilberto Pdf
Natural language processing (NLP) is a branch of artificial intelligence that has emerged as a prevalent method of practice for a sizeable amount of companies. NLP enables software to understand human language and process complex data that is generated within businesses. In a competitive market, leading organizations are showing an increased interest in implementing this technology to improve user experience and establish smarter decision-making methods. Research on the application of intelligent analytics is crucial for professionals and companies who wish to gain an edge on the opposition. The Handbook of Research on Natural Language Processing and Smart Service Systems is a collection of innovative research on the integration and development of intelligent software tools and their various applications within professional environments. While highlighting topics including discourse analysis, information retrieval, and advanced dialog systems, this book is ideally designed for developers, practitioners, researchers, managers, engineers, academicians, business professionals, scholars, policymakers, and students seeking current research on the improvement of competitive practices through the use of NLP and smart service systems.
