Natural Language Processing With Python

Author: Steven Bird
Publisher: "O'Reilly Media, Inc."
ISBN: 0596555717
Size: 49.71 MB
Format: PDF, Kindle
View: 1042
Download
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.

Natural Language Processing Fundamentals

Author: Sohom Ghosh
Publisher: Packt Publishing Ltd
ISBN: 178995598X
Size: 18.56 MB
Format: PDF, Mobi
View: 4188
Download
Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems. Key Features Assimilate key NLP concepts and terminologies Explore popular NLP tools and techniques Gain practical experience using NLP in application code Book Description If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language. What you will learn Obtain, verify, and clean data before transforming it into a correct format for use Perform data analysis and machine learning tasks using Python Understand the basics of computational linguistics Build models for general natural language processing tasks Evaluate the performance of a model with the right metrics Visualize, quantify, and perform exploratory analysis from any text data Who this book is for Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.

Text Analytics With Python

Author: Dipanjan Sarkar
Publisher: Apress
ISBN: 1484223888
Size: 41.16 MB
Format: PDF, ePub
View: 2382
Download
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data

Advances In Natural Language Processing

Author: Adam Przepiórkowski
Publisher: Springer
ISBN: 3319108883
Size: 80.35 MB
Format: PDF, Kindle
View: 4719
Download
This book constitutes the refereed proceedings of the 9th International Conference on Advances in Natural Language Processing, PolTAL 2014, Warsaw, Poland, in September 2014. The 27 revised full papers and 20 revised short papers presented were carefully reviewed and selected from 83 submissions. The papers are organized in topical sections on morphology, named entity recognition, term extraction; lexical semantics; sentence level syntax, semantics, and machine translation; discourse, coreference resolution, automatic summarization, and question answering; text classification, information extraction and information retrieval; and speech processing, language modelling, and spell- and grammar-checking.

Natural Language Processing And Information Systems

Author: Max Silberztein
Publisher: Springer
ISBN: 3319919474
Size: 46.52 MB
Format: PDF, ePub, Docs
View: 1915
Download
This book constitutes the refereed proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, held in Paris, France, in June 2018. The 18 full papers, 26 short papers, and 9 poster papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based Models and Applications; Neural Networks Based Approaches; Ontology Engineering; NLP; Text Similarities and Plagiarism Detection; Text Classification; Information Mining; Recommendation Systems; Translation and Foreign Language Querying; Software Requirement and Checking.

Natural Language Processing With Pytorch

Author: Delip Rao
Publisher: "O'Reilly Media, Inc."
ISBN: 149197818X
Size: 31.21 MB
Format: PDF, Mobi
View: 5421
Download
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

Modeling Techniques In Predictive Analytics With Python And R

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Size: 39.69 MB
Format: PDF, ePub, Docs
View: 6509
Download
Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Nltk Essentials

Author: Nitin Hardeniya
Publisher: Packt Publishing Ltd
ISBN: 1784398500
Size: 75.62 MB
Format: PDF, Mobi
View: 6178
Download
Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers and human languages. With the instances of human-computer interaction increasing, it's becoming imperative for computers to comprehend all major natural languages. Natural Language Toolkit (NLTK) is one such powerful and robust tool. You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text. By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.

Web And Network Data Science

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133887642
Size: 16.22 MB
Format: PDF, ePub, Mobi
View: 531
Download
Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Modeling Techniques In Predictive Analytics

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133886190
Size: 56.89 MB
Format: PDF, Kindle
View: 3304
Download
To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more