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020 _a9783319257419
_9978-3-319-25741-9
024 7 _a10.1007/978-3-319-25741-9
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
_2bicssc
072 7 _aCOM030000
_2bisacsh
082 0 4 _a025.04
_223
100 1 _aWachsmuth, Henning.
_eauthor.
245 1 0 _aText Analysis Pipelines
_h[electronic resource] :
_bTowards Ad-hoc Large-Scale Text Mining /
_cby Henning Wachsmuth.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXX, 302 p. 74 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v9383
520 _aThis monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics. Both web search and big data analytics aim to fulfill peoples' needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.
650 0 _aComputer science.
650 0 _aComputers.
650 0 _aMathematical logic.
650 0 _aDatabase management.
650 0 _aInformation storage and retrieval.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aInformation Systems Applications (incl. Internet).
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aMathematical Logic and Formal Languages.
650 2 4 _aDatabase Management.
650 2 4 _aComputation by Abstract Devices.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319257402
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v9383
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-25741-9
912 _aZDB-2-SCS
912 _aZDB-2-LNC
942 _cEBK
999 _c57353
_d57353