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Text analysis pipelines[electronic resource] :towards ad-hoc large scale text mining /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	025.04
書名/作者:	Text analysis pipelines : towards ad-hoc large scale text mining // by Henning Wachsmuth.
作者:	Wachsmuth, Henning.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xx, 302 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
標題:	Text processing (Computer science)
標題:	Computer science.
標題:	Computers.
標題:	Logic, Symbolic and mathematical.
標題:	Database management.
標題:	Information storage and retrieval.
標題:	Artificial intelligence.
標題:	Information Systems Applications (incl. Internet)
標題:	Artificial Intelligence (incl. Robotics)
標題:	Mathematical Logic and Formal Languages.
標題:	Computation by Abstract Devices.
ISBN:	9783319257419
ISBN:	9783319257402
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-25741-9

Text analysis pipelines[electronic resource] :towards ad-hoc large scale text mining /
Wachsmuth, Henning.

Text analysis pipelinestowards ad-hoc large scale text mining /[electronic resource] :by Henning Wachsmuth. - Cham :Springer International Publishing :2015. - xx, 302 p. :ill., digital ;24 cm. - Lecture notes in computer science,93830302-9743 ;. - Lecture notes in computer science ;7103..

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.

ISBN: 9783319257419

Standard No.: 10.1007/978-3-319-25741-9doiSubjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.T48

Dewey Class. No.: 025.04

Text analysis pipelines[electronic resource] :towards ad-hoc large scale text mining /
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