A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for Nlp, Gomez-Perez Jose Manuel, Denaux Ronald, Garcia-Silva Andres

Автор: Vajjala Sowmya, Majumder Bodhisattwa, Gupta Anuj
Название: Practical Natural Language Processing: A Pragmatic Approach to Processing and Analyzing Language Data
ISBN: 1492054054 ISBN-13(EAN): 9781492054054
Издательство: Wiley
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Цена: 67570.00 T
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Описание: Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide.

Автор: Chopra Rohan, Godbole Aniruddha M., Sadvilkar Nipun
Название: The Natural Language Processing Workshop: Confidently design and build your own NLP projects with this easy-to-understand practical guide
ISBN: 1800208421 ISBN-13(EAN): 9781800208421
Издательство: Неизвестно
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Цена: 53940.00 T
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Описание: The Natural Language Processing Workshop takes you through fundamental NLP techniques, such as preparing datasets, collecting text, extracting text, and sentiment analysis. As you progress, you`ll get to grips with creating your own chatbots and dynamic models.

Автор: Goldberg Yoav
Название: Neural Network Methods in Natural Language Processing
ISBN: 1627052984 ISBN-13(EAN): 9781627052986
Издательство: Mare Nostrum (Eurospan)
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Цена: 76690.00 T
Наличие на складе: Нет в наличии.
Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Автор: Sankar Krishna, Jackovich Jeffrey, Richards Ruze
Название: The Applied AI and Natural Language Processing Workshop: Explore practical ways to transform your simple projects into powerful intelligent applicatio
ISBN: 180020874X ISBN-13(EAN): 9781800208742
Издательство: Неизвестно
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Цена: 53940.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Applied AI and NLP Workshop will show you how to integrate artificial intelligence with Amazon Web Services to create intelligent applications. From developing language translation apps and chatbots to creating models for processing large volumes of images, you`ll learn key concepts effectively and in a real-world context.

Автор: Rothman Denis
Название: Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBER
ISBN: 1800565798 ISBN-13(EAN): 9781800565791
Издательство: Неизвестно
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Цена: 122600.00 T
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Описание: This volume reports on excavations in advance of the development of a site in Norton-on-Derwent, North Yorkshire close to the line of the main Roman road running from the crossing point of the River Derwent near Malton Roman fort to York. This site provided much additional information on aspects of the poorly understood `small town` of Delgovicia.

Автор: Tecuci
Название: Knowledge Engineering
ISBN: 1107122562 ISBN-13(EAN): 9781107122567
Издательство: Cambridge Academ
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Цена: 78150.00 T
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Описание: This book presents a significant advancement in knowledge engineering based on learning agent technology. Using the software Disciple-EBR, students, practitioners, and researchers can rapidly develop learning assistants in numerous domains that require evidence-based reasoning, including cyber security, law, forensics, medicine, and education.

Автор: Janev Valentina, Graux Damien, Jabeen Hajira
Название: Knowledge Graphs and Big Data Processing
ISBN: 3030531988 ISBN-13(EAN): 9783030531980
Издательство: Springer
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Цена: 37260.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Foundations.- Chapter 1. Ecosystem of Big Data.- Chapter 2. Knowledge Graphs: The Layered Perspective.- Chapter 3. Big Data Outlook, Tools, and Architectures.- Architecture.- Chapter 4. Creation of Knowledge Graphs.- Chapter 5. Federated Query Processing.- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight.- Methods and Solutions.- Chapter 7. Scalable Knowledge Graph Processing using SANSA.- Chapter 8. Context-Based Entity Matching for Big Data.- Applications.- Chapter 9. Survey on Big Data Applications.- Chapter 10. Case Study from the Energy Domain.

Автор: Lotem Peled-Cohen, Roi Reichart, Rotem Dror, Segev Shlomov
Название: Statistical Significance Testing for Natural Language Processing
ISBN: 1681737973 ISBN-13(EAN): 9781681737973
Издательство: Mare Nostrum (Eurospan)
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Цена: 66530.00 T
Наличие на складе: Нет в наличии.
Описание: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental.

The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.

Автор: Sumathi S, Janani M
Название: Neural Networks for Natural Language Processing
ISBN: 1799811603 ISBN-13(EAN): 9781799811602
Издательство: Mare Nostrum (Eurospan)
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Цена: 175560.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: Lotem Peled-Cohen, Roi Reichart, Rotem Dror, Segev Shlomov
Название: Statistical Significance Testing for Natural Language Processing
ISBN: 1681737957 ISBN-13(EAN): 9781681737959
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 48050.00 T
Наличие на складе: Нет в наличии.
Описание: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental.

The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.

Автор: Sumathi S, Janani M
Название: Neural Networks for Natural Language Processing
ISBN: 179981159X ISBN-13(EAN): 9781799811596
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 229150.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: Sri, Mathangi
Название: Practical natural language processing with python
ISBN: 148426245X ISBN-13(EAN): 9781484262450
Издательство: Springer
Рейтинг:
Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Chapter 1: Text Data in Real Word