Information Extraction And Knowledge Graph Development For Manufacturing Science Domain Using Natural Language Processing

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Data Science and Intelligent Systems

Author : Radek Silhavy
Publisher : Springer Nature
ISBN 13 : 3030903214
Total Pages : 1073 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Data Science and Intelligent Systems by : Radek Silhavy

Download or read book Data Science and Intelligent Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2021-11-16 with total page 1073 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results

Knowledge Graphs and Big Data Processing

Author : Valentina Janev
Publisher : Springer Nature
ISBN 13 : 3030531996
Total Pages : 212 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Knowledge Graphs and Big Data Processing by : Valentina Janev

Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer Nature. This book was released on 2020-07-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Proceedings of Fourth International Conference on Inventive Material Science Applications

Author : V. Bindhu
Publisher : Springer Nature
ISBN 13 : 9811643210
Total Pages : 802 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Proceedings of Fourth International Conference on Inventive Material Science Applications by : V. Bindhu

Download or read book Proceedings of Fourth International Conference on Inventive Material Science Applications written by V. Bindhu and published by Springer Nature. This book was released on 2021-10-19 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume is a collection of best selected research papers presented at the 4th International Conference on Inventive Material Science Applications (ICIMA 2021) organized by PPG Institute of Technology, Coimbatore, India during 14 – 15 May 2021. The book includes original research by material science researchers towards developing a compact and efficient functional elements and structures for micro, nano and optoelectronic applications. The book covers important topics like nanomaterials and devices, optoelectronics, sustainable electronic materials, nanocomposites and nanostructures, hybrid electronic materials, medical electronics, computational material science, wearable electronic devices and models, and optical/nano-sensors.

Natural Language Processing and Text Mining

Author : Anne Kao
Publisher : Springer Science & Business Media
ISBN 13 : 1846287545
Total Pages : 272 pages
Book Rating : 4.8/5 (462 download)

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Book Synopsis Natural Language Processing and Text Mining by : Anne Kao

Download or read book Natural Language Processing and Text Mining written by Anne Kao and published by Springer Science & Business Media. This book was released on 2007-03-06 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.

Applied Natural Language Processing in the Enterprise

Author : Ankur A. Patel
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492062545
Total Pages : 336 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Applied Natural Language Processing in the Enterprise by : Ankur A. Patel

Download or read book Applied Natural Language Processing in the Enterprise written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2021-05-12 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

Graph-based Natural Language Processing and Information Retrieval

Author : Rada Mihalcea
Publisher : Cambridge University Press
ISBN 13 : 1139498827
Total Pages : 201 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Graph-based Natural Language Processing and Information Retrieval by : Rada Mihalcea

Download or read book Graph-based Natural Language Processing and Information Retrieval written by Rada Mihalcea and published by Cambridge University Press. This book was released on 2011-04-11 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Speech & Language Processing

Author : Dan Jurafsky
Publisher : Pearson Education India
ISBN 13 : 9788131716724
Total Pages : 912 pages
Book Rating : 4.7/5 (167 download)

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Book Synopsis Speech & Language Processing by : Dan Jurafsky

Download or read book Speech & Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Management in Pervasive Systems

Author : Francesco Colace
Publisher : Springer
ISBN 13 : 3319200623
Total Pages : 380 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Data Management in Pervasive Systems by : Francesco Colace

Download or read book Data Management in Pervasive Systems written by Francesco Colace and published by Springer. This book was released on 2015-10-17 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contributes to illustrating the methodological and technological issues of data management in Pervasive Systems by using the DataBenc project as the running case study for a variety of research contributions: sensor data management, user-originated data operation and reasoning, multimedia data management, data analytics and reasoning for event detection and decision making, context modelling and control, automatic data and service tailoring for personalization and recommendation. The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.

Natural Language Processing for the Semantic Web

Author : Diana Maynard
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627056327
Total Pages : 196 pages
Book Rating : 4.6/5 (27 download)

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Book Synopsis Natural Language Processing for the Semantic Web by : Diana Maynard

Download or read book Natural Language Processing for the Semantic Web written by Diana Maynard and published by Morgan & Claypool Publishers. This book was released on 2016-12-13 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks. The first half of this book describes the basic NLP processing tools: tokenization, part-of-speech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. The second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. These chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. The book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future.

Programming the Semantic Web

Author : Toby Segaran
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449379176
Total Pages : 302 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Programming the Semantic Web by : Toby Segaran

Download or read book Programming the Semantic Web written by Toby Segaran and published by "O'Reilly Media, Inc.". This book was released on 2009-07-09 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this book, the promise of the Semantic Web -- in which machines can find, share, and combine data on the Web -- is not just a technical possibility, but a practical reality Programming the Semantic Web demonstrates several ways to implement semantic web applications, using current and emerging standards and technologies. You'll learn how to incorporate existing data sources into semantically aware applications and publish rich semantic data. Each chapter walks you through a single piece of semantic technology and explains how you can use it to solve real problems. Whether you're writing a simple mashup or maintaining a high-performance enterprise solution,Programming the Semantic Web provides a standard, flexible approach for integrating and future-proofing systems and data. This book will help you: Learn how the Semantic Web allows new and unexpected uses of data to emerge Understand how semantic technologies promote data portability with a simple, abstract model for knowledge representation Become familiar with semantic standards, such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL) Make use of semantic programming techniques to both enrich and simplify current web applications

Exploiting Semantic Web Knowledge Graphs in Data Mining

Author : P. Ristoski
Publisher : IOS Press
ISBN 13 : 1614999813
Total Pages : 246 pages
Book Rating : 4.6/5 (149 download)

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Book Synopsis Exploiting Semantic Web Knowledge Graphs in Data Mining by : P. Ristoski

Download or read book Exploiting Semantic Web Knowledge Graphs in Data Mining written by P. Ristoski and published by IOS Press. This book was released on 2019-06-28 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.

Getting Started with Natural Language Processing

Author : Ekaterina Kochmar
Publisher : Simon and Schuster
ISBN 13 : 1638350922
Total Pages : 454 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Getting Started with Natural Language Processing by : Ekaterina Kochmar

Download or read book Getting Started with Natural Language Processing written by Ekaterina Kochmar and published by Simon and Schuster. This book was released on 2022-11-15 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hit the ground running with this in-depth introduction to the NLP skills and techniques that allow your computers to speak human. In Getting Started with Natural Language Processing you’ll learn about: Fundamental concepts and algorithms of NLP Useful Python libraries for NLP Building a search algorithm Extracting information from raw text Predicting sentiment of an input text Author profiling Topic labeling Named entity recognition Getting Started with Natural Language Processing is an enjoyable and understandable guide that helps you engineer your first NLP algorithms. Your tutor is Dr. Ekaterina Kochmar, lecturer at the University of Bath, who has helped thousands of students take their first steps with NLP. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. If you’re a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you. About the technology From smart speakers to customer service chatbots, apps that understand text and speech are everywhere. Natural language processing, or NLP, is the key to this powerful form of human/computer interaction. And a new generation of tools and techniques make it easier than ever to get started with NLP! About the book Getting Started with Natural Language Processing teaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you’ll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you’ve previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get started—even if you have no background in machine learning! What's inside Fundamental concepts and algorithms of NLP Extracting information from raw text Useful Python libraries Topic labeling Building a search algorithm About the reader You’ll need basic Python skills. No experience with NLP required. About the author Ekaterina Kochmar is a lecturer at the Department of Computer Science of the University of Bath, where she is part of the AI research group. Table of Contents 1 Introduction 2 Your first NLP example 3 Introduction to information search 4 Information extraction 5 Author profiling as a machine-learning task 6 Linguistic feature engineering for author profiling 7 Your first sentiment analyzer using sentiment lexicons 8 Sentiment analysis with a data-driven approach 9 Topic analysis 10 Topic modeling 11 Named-entity recognition

Domain-Specific Knowledge Graph Construction

Author : Mayank Kejriwal
Publisher : Springer
ISBN 13 : 3030123758
Total Pages : 115 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Domain-Specific Knowledge Graph Construction by : Mayank Kejriwal

Download or read book Domain-Specific Knowledge Graph Construction written by Mayank Kejriwal and published by Springer. This book was released on 2019-03-04 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as an accessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.

Hands-On Natural Language Processing with Python

Author : Rajesh Arumugam
Publisher : Packt Publishing Ltd
ISBN 13 : 1789135915
Total Pages : 307 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Hands-On Natural Language Processing with Python by : Rajesh Arumugam

Download or read book Hands-On Natural Language Processing with Python written by Rajesh Arumugam and published by Packt Publishing Ltd. This book was released on 2018-07-18 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

The Text Mining Handbook

Author : Ronen Feldman
Publisher : Cambridge University Press
ISBN 13 : 0521836573
Total Pages : 423 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis The Text Mining Handbook by : Ronen Feldman

Download or read book The Text Mining Handbook written by Ronen Feldman and published by Cambridge University Press. This book was released on 2007 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher description

Knowledge Graphs

Author : Dieter Fensel
Publisher : Springer Nature
ISBN 13 : 3030374394
Total Pages : 156 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Knowledge Graphs by : Dieter Fensel

Download or read book Knowledge Graphs written by Dieter Fensel and published by Springer Nature. This book was released on 2020-01-31 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources. Chapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema.org to specific domains and tasks. To illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction to knowledge graphs and their implementation.

Mathematics for Machine Learning

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.