2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing Dez 2015], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPRO… Mehr…
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2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; … Mehr…
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2015, ISBN: 9783319257402
Kartoniert, 324 Seiten, 235mm x 155mm x 18mm, Sprache(n): eng Includes supplementary material: sn.pub/extrasThis monograph proposes a comprehensive and fully automatic approach to designi… Mehr…
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2015, ISBN: 3319257404
This 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 … Mehr…
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2015, ISBN: 3319257404
1st ed. 2015 Kartoniert / Broschiert Data Mining (EDV), Datenbanken, Informationsrückgewinnung, Information Retrieval, Angewandte Informatik, Theoretische Informatik, Künstliche Intelli… Mehr…
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2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing Dez 2015], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPRO… Mehr…
2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; … Mehr…
2015
ISBN: 9783319257402
Kartoniert, 324 Seiten, 235mm x 155mm x 18mm, Sprache(n): eng Includes supplementary material: sn.pub/extrasThis monograph proposes a comprehensive and fully automatic approach to designi… Mehr…
2015, ISBN: 3319257404
This 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 … Mehr…
2015, ISBN: 3319257404
1st ed. 2015 Kartoniert / Broschiert Data Mining (EDV), Datenbanken, Informationsrückgewinnung, Information Retrieval, Angewandte Informatik, Theoretische Informatik, Künstliche Intelli… Mehr…
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This 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.
Detailangaben zum Buch - Text Analysis Pipelines
EAN (ISBN-13): 9783319257402
ISBN (ISBN-10): 3319257404
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2015
Herausgeber: Springer International Publishing
Buch in der Datenbank seit 2015-09-29T21:29:31+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-12-12T23:23:41+01:00 (Berlin)
ISBN/EAN: 9783319257402
ISBN - alternative Schreibweisen:
3-319-25740-4, 978-3-319-25740-2
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: wachsmuth, henning, wachsmut
Titel des Buches: text mining, lecture notes computer science, hoc, scale
Daten vom Verlag:
Autor/in: Henning Wachsmuth
Titel: Theoretical Computer Science and General Issues; Lecture Notes in Computer Science; Text Analysis Pipelines - Towards Ad-hoc Large-Scale Text Mining
Verlag: Springer; Springer International Publishing
302 Seiten
Erscheinungsjahr: 2015-12-04
Cham; CH
Gedruckt / Hergestellt in Niederlande.
Gewicht: 0,498 kg
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XX, 302 p. 74 illus. in color.
BC; Information Storage and Retrieval; Hardcover, Softcover / Informatik, EDV/Informatik; Informationsrückgewinnung, Information Retrieval; Verstehen; Efficienciy and effectiveness; Information extraction; Machine learning; Natural language processing; Text minig; Adaptive scheduling; Argumentation structure; Artificial intelligence; Big data; Domain robustness; Information structure; Language function analysis; Online learning; Parallelization; Pipeline; Pipeline design; Sentiment analysis; Similarity; Text analysis; Text classification; Information Systems Applications (incl. Internet); Artificial Intelligence; Mathematical Logic and Formal Languages; Database Management; Computation by Abstract Devices; Information Storage and Retrieval; Computer and Information Systems Applications; Artificial Intelligence; Formal Languages and Automata Theory; Database Management; Theory of Computation; Data Warehousing; Angewandte Informatik; Künstliche Intelligenz; Theoretische Informatik; Datenbanken; EA
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.
Includes supplementary material: sn.pub/extras
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