2015, ISBN: 3319257404

[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing Dez 2015], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPRO… More...

[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing Dez 2015], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; TEXTMINIG; ADAPTIVESCHEDULING; ARGUMENTATIONSTRUCTURE; ARTIFICIALINTELLIGENCE; BIGDATA; DOMAINROBUSTNESS; INFORMATIONSTRUCTURE; LANGUAGEFUNCTIONANALYSIS; ONLINELEARNING; PARALLELIZATION; PIPELINE; PIPELINEDESIGN; SENTIMENTANALYSIS; SIMILARITY; TEXTANALYSIS; TEXTCLASSIFICATION, This item is printed on demand - it takes 3-4 days longer - Neuware -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. 324 pp. Englisch, Books<

2015, ISBN: 3319257404

[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; … More...

[EAN: 9783319257402], Neubuch, [PU: Springer International Publishing], DATA MINING (EDV); EFFICIENCIYANDEFFECTIVENESS; INFORMATIONEXTRACTION; MACHINELEARNING; NATURALLANGUAGEPROCESSING; TEXTMINIG; ADAPTIVESCHEDULING; ARGUMENTATIONSTRUCTURE; ARTIFICIALINTELLIGENCE; BIGDATA; DOMAINROBUSTNESS; INFORMATIONSTRUCTURE; LANGUAGEFUNCTIONANALYSIS; ONLINELEARNING; PARALLELIZATION; PIPELINE; PIPELINEDESIGN; SENTIMENTANALYSIS; SIMILARITY; TEXTANALYSIS; TEXTCLASSIFICATION, Druck auf Anfrage Neuware - Printed after ordering - 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., Books<

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… More...

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 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. Versandkostenfreie Lieferung Efficienciyandeffectiveness, informationextraction, machinelearning, NaturalLanguageProcessing, Textminig, Adaptivescheduling, Argumentationstructure, ArtificialIntelligence, Bigdata, Domainrobustness, InformationStructure, Languagefunctionanalysis, onlinelearning, Parallelization, Pipeline, PipelineDesign, sentimentanalysis, Similarity, Textanalysis, TextClassification, Data Mining (EDV)<

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 … More...

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. Media Buch, 302 Seiten, Media > Books, Springer International Publishing, 2015<

2015, ISBN: 3319257404

1st ed. 2015 Kartoniert / Broschiert Data Mining (EDV), Datenbanken, Informationsrückgewinnung, Information Retrieval, Angewandte Informatik, Theoretische Informatik, Künstliche Intelli… More...

1st ed. 2015 Kartoniert / Broschiert Data Mining (EDV), Datenbanken, Informationsrückgewinnung, Information Retrieval, Angewandte Informatik, Theoretische Informatik, Künstliche Intelligenz, Efficienciyandeffectiveness; informationextraction; machinelearning; NaturalLanguageProcessing; Textminig; Adaptivescheduling; Argumentationstructure; ArtificialIntelligence; Bigdata; Domainrobustness; InformationStructure; Languagefunctionanalysis; onlinelearning; Parallelization; Pipeline; PipelineDesign; sentimentanalysis; Similarity; Textanalysis; TextClassification, mit Schutzumschlag 11, [PU:Springer International Publishing; Springer International Publishing AG]<