000 03891nam a2200649 i 4500
001 6354187
003 IEEE
005 20220712204804.0
006 m o d
007 cr |n|||||||||
008 151223s2012 mau ob 001 eng d
020 _a9780262305167
_qelectronic
020 _a0262017695
020 _a9780262017695
020 _z026230516X
_qelectronic
035 _a(CaBNVSL)mat06354187
035 _a(IDAMS)0b00006481b4dbd5
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aR118.6
_b.S53 2012eb
082 0 4 _a610.285
_223
100 1 _aShatkay, Hagit,
_eauthor.
_923922
245 1 0 _aMining the biomedical literature /
_cHagit Shatkay and Mark Craven.
264 1 _aCambridge, Massachusetts :
_bMIT Press,
_cc2012.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2012]
300 _a1 PDF (150 pages).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aComputational molecular biology
504 _aIncludes bibliographical references and index.
505 0 _aFundamental Concepts in Biomedical Text Analysis -- Information Retrieval -- Information Extraction -- Evaluation -- Putting it All Together : Current Applications and Future Directions.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
520 _aThe 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.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aDescription based on PDF viewed 12/23/2015.
650 0 _aInformation retrieval.
_910134
650 0 _aContent analysis (Communication)
_923923
650 0 _aInformation storage and retrieval systems
_xBiology.
_923924
650 0 _aInformation storage and retrieval systems
_xMedicine.
_910978
650 0 _aBioinformatics.
_99561
650 0 _aMedical informatics.
_94729
650 0 _aData mining.
_93907
650 0 _aBiological literature
_xData processing.
_923925
650 0 _aMedical literature
_xData processing.
_923926
650 1 2 _aData Mining.
_93907
650 2 2 _aInformation Storage and Retrieval.
_923927
650 2 2 _aMedical Informatics.
_94729
655 0 _aElectronic books.
_93294
700 1 _aCraven, Mark.
_923928
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_923929
710 2 _aMIT Press,
_epublisher.
_923930
776 0 8 _iPrint version
_z9780262017695
830 0 _aComputational molecular biology
_922185
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6354187
942 _cEBK
999 _c73302
_d73302