2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing Dez 2015], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPRO… Plus…
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2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; … Plus…
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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… Plus…
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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 … Plus…
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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… Plus…
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2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing Dez 2015], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPRO… Plus…
Henning Wachsmuth:
Text Analysis Pipelines : Towards Ad-hoc Large-Scale Text Mining - Livres de poche2015, ISBN: 3319257404
[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; … Plus…
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… Plus…
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 … Plus…
2015, ISBN: 3319257404
1st ed. 2015 Kartoniert / Broschiert Data Mining (EDV), Datenbanken, Informationsrückgewinnung, Information Retrieval, Angewandte Informatik, Theoretische Informatik, Künstliche Intelli… Plus…
Données bibliographiques du meilleur livre correspondant
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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.
Informations détaillées sur le livre - Text Analysis Pipelines
EAN (ISBN-13): 9783319257402
ISBN (ISBN-10): 3319257404
Version reliée
Livre de poche
Date de parution: 2015
Editeur: Springer International Publishing
Livre dans la base de données depuis 2015-09-29T21:29:31+02:00 (Zurich)
Page de détail modifiée en dernier sur 2023-12-12T23:23:41+01:00 (Zurich)
ISBN/EAN: 9783319257402
ISBN - Autres types d'écriture:
3-319-25740-4, 978-3-319-25740-2
Autres types d'écriture et termes associés:
Auteur du livre: wachsmuth, henning, wachsmut
Titre du livre: text mining, lecture notes computer science, hoc, scale
Données de l'éditeur
Auteur: Henning Wachsmuth
Titre: Lecture Notes in Computer Science; Theoretical Computer Science and General Issues; Text Analysis Pipelines - Towards Ad-hoc Large-Scale Text Mining
Editeur: Springer; Springer International Publishing
302 Pages
Date de parution: 2015-12-04
Cham; CH
Imprimé / Fabriqué en
Langue: Anglais
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XX, 302 p. 74 illus. in color.
BC; 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 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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