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Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0001
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0002
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0003
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0004
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0005
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0006
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0007
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0008
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.003.0009
EISBN: 9780262305167
Publisher: The MIT Press
Published: 10 August 2012
DOI: 10.7551/mitpress/7438.001.0001
EISBN: 9780262305167
A concise introduction to fundamental methods for finding and extracting relevant information from the ever-increasing amounts of biomedical text available The introduction of high-throughput methods has transformed biology into a data-rich science. Knowledge about biological entities and processes has traditionally been acquired by thousands of scientists through decades of experimentation and analysis. The current abundance of biomedical data is accompanied by the creation and quick dissemination of new information. Much of this information and knowledge, however, is represented only in text form—in the biomedical literature, lab notebooks, Web pages, and other sources. Researchers' need to find relevant information in the vast amounts of text has created a surge of interest in automated text-analysis. In this book, Hagit Shatkay and Mark Craven offer a concise and accessible introduction to key ideas in biomedical text mining. The chapters cover such topics as the relevant sources of biomedical text; text-analysis methods in natural language processing; the tasks of information extraction, information retrieval, and text categorization; and methods for empirically assessing text-mining systems. Finally, the authors describe several applications that recognize entities in text and link them to other entities and data resources, support the curation of structured databases, and make use of text to enable further prediction and discovery.