Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
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 softw...
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
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New York, NY, USA
Association for Computing Machinery and Morgan & Claypool
2016
ACM Books Morgan & Claypool Association for Computing Machinery Association for Computing Machinery and Morgan & C |
| Vydání: | 1 |
| Edice: | ACM Books |
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| ISBN: | 9781970001174, 1970001178, 197000116X, 9781970001167 |
| ISSN: | 2374-6777 |
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| Abstract | 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 manage and analyze 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 capture 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 management and analysis 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 many of these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. Because humans can understand natural languages far better than computers can, effective involvement of humans in a text information system is generally needed and text information systems often serve as intelligent assistants for humans. Depending on how a text information system collaborates with humans, we distinguish two kinds of text information systems. The first is information retrieval systems which include search engines and recommender systems; they assist users in finding from a large collection of text data the most relevant text data that are actually needed for solving a specific application problem, thus effecively turning big raw text data into much smaller relevant text data that can be more easily processed by humans. The second is text mining application systems; they can assist users in analyzing patterns in text data to extract and discover useful actionable knowledge directly useful for task completion or decision making, thus providing more direct task support for users. This book covers the major concepts, techniques, and ideas in information retrieval and text data mining 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 information retrieval and text mining to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. This book can be used as a textbook for computer science undergraduates and graduates, library and information scientists, or as a reference book for practitioners working on relevant problems in managing and analyzing text data. |
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| AbstractList | 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 manage and analyze 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 capture 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 management and analysis 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 many of these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. Because humans can understand natural languages far better than computers can, effective involvement of humans in a text information system is generally needed and text information systems often serve as intelligent assistants for humans. Depending on how a text information system collaborates with humans, we distinguish two kinds of text information systems. The first is information retrieval systems which include search engines and recommender systems; they assist users in finding from a large collection of text data the most relevant text data that are actually needed for solving a specific application problem, thus effecively turning big raw text data into much smaller relevant text data that can be more easily processed by humans. The second is text mining application systems; they can assist users in analyzing patterns in text data to extract and discover useful actionable knowledge directly useful for task completion or decision making, thus providing more direct task support for users. This book covers the major concepts, techniques, and ideas in information retrieval and text data mining 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 information retrieval and text mining to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. This book can be used as a textbook for computer science undergraduates and graduates, library and information scientists, or as a reference book for practitioners working on relevant problems in managing and analyzing text data. This book provides a systematic introduction to a wide range of statistical and heuristical approaches to the management and analysis of text data. It emphasizes the most useful knowledge and skills required to build a variety of practically useful text information systems. Because humans can understand natural languages far better than computers can, effective involvement of humans in a text information system is generally needed and text information systems often serve as intelligent assistants for humans. Depending on how a text information system collaborates with humans, we distinguish two kinds of text information systems. The first is information retrieval systems which include search engines and recommender systems; they assist users in finding from a large collection of text data the most relevant text data that are actually needed for solving a specific application problem, thus effectively turning big raw text data into much smaller relevant text data that can be more easily processed by humans. The second is text mining application systems; they can assist users in analyzing patterns in text data to extract and discover useful actionable knowledge directly useful for task completion or decision making, thus providing more direct task support for users. The book covers the major concepts, techniques, and ideas in information retrieval and text data mining 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 information retrieval and text mining 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 computer science undergraduates and graduates, library and information scientists, or as a reference book for practitioners working on relevant problems in managing and analyzing text data. 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. |
| Author | Massung, Sean Zhai, ChengXiang |
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| Notes | Includes bibliographical references (p. [477]-488) and index |
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| Snippet | Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise... This book provides a systematic introduction to a wide range of statistical and heuristical approaches to the management and analysis of text data. It... |
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| SubjectTerms | Computational linguistics-Statistical methods Data mining |
| Title | Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining |
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