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Text processing (Computer science) --- Processing, Text (Computer science) --- Database management --- Electronic data processing --- Information storage and retrieval systems --- Word processing

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In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such

Text processing (Computer science) --- Sorting (Electronic computers) --- Relevance --- Database searching --- Search engines --- Engineering & Applied Sciences --- Computer Science --- Programming --- Relevance. --- Database searching. --- Programming. --- Computer programming --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Electronic information resource searching --- Pertinence --- Relevancy --- Meaning (Philosophy) --- Meaning (Psychology) --- Computer sorting --- Electronic data processing --- Processing, Text (Computer science) --- Database management --- Information storage and retrieval systems --- Word processing

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Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the m...

Text processing (Computer science) --- R (Computer program language) --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- GNU-S (Computer program language) --- Domain-specific programming languages --- Processing, Text (Computer science) --- Database management --- Electronic data processing --- Information storage and retrieval systems --- Word processing

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Text processing (Computer science) --- Digital video. --- Computer vision. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Digital motion video --- PC video --- Video, Digital --- Computer graphics --- Digital media --- Multimedia systems --- Processing, Text (Computer science) --- Database management --- Electronic data processing --- Information storage and retrieval systems --- Word processing --- Digital techniques

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A regular expression (regex) is a pattern that describes a set of strings. Regular expressions are used for advanced context-sensitive searches (e.g. parsing data streams, data mining) and text modifications. They can be found in many advanced editors (e.g. vi, Emacs), in parser programs (e.g. grep) and in languages (e.g. Perl), mostly in a UNIX environment. This book is the standard work on regexes.

Programming --- Computer programming --- Perl (Computer program language) --- Text processing (Computer science) --- 681.3 --- Internet : scripttalen --- Programmeren --- Processing, Text (Computer science) --- Database management --- Electronic data processing --- Information storage and retrieval systems --- Word processing --- Pathologically Eclectic Rubbish Lister (Computer program language) --- Practical Extraction and Report Language (Computer program language) --- Scripting languages (Computer science) --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Regular expressions --- 004.4 --- Reguliere expressie