Incentive-Centric Semantic Web Application Engineering

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Internet
Cover of the book Incentive-Centric Semantic Web Application Engineering by Elena Simperl, Roberta Cuel, Martin Stein, Morgan & Claypool Publishers
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Elena Simperl, Roberta Cuel, Martin Stein ISBN: 9781627051866
Publisher: Morgan & Claypool Publishers Publication: January 25, 2013
Imprint: Morgan & Claypool Publishers Language: English
Author: Elena Simperl, Roberta Cuel, Martin Stein
ISBN: 9781627051866
Publisher: Morgan & Claypool Publishers
Publication: January 25, 2013
Imprint: Morgan & Claypool Publishers
Language: English

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.

This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.

This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

More books from Morgan & Claypool Publishers

Cover of the book Experience Design by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Keplerian Ellipses by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Mitigation of Cancer Therapy Side-Effects with Light by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Neural Network Methods in Natural Language Processing by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book A Tour of the Subatomic Zoo by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Resource-Oriented Architecture Patterns for Webs of Data by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Hard Problems in Software Testing by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Blocks and Chains by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book A Little Book on Teaching by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Essential Classical Mechanics for Device Physics by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Physics and Video Analysis by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Photomedicine and Stem Cells by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Classical Theory of Free-Electron Lasers by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Discrete Quantum Mechanics by Elena Simperl, Roberta Cuel, Martin Stein
Cover of the book Web Corpus Construction by Elena Simperl, Roberta Cuel, Martin Stein
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy