Text Analysis Pipelines [electronic resource] : Towards Ad-hoc Large-Scale Text Mining / by Henning Wachsmuth.
By: Wachsmuth, Henning [author.]
.
Contributor(s): SpringerLink (Online service).
Material type: 
BookSeries: Theoretical Computer Science and General Issues: 9383Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015.Description: XX, 302 p. 74 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319257419.Subject(s): Information storage and retrieval systems | Application software | Artificial intelligence | Machine theory | Database management | Computer science | Information Storage and Retrieval | Computer and Information Systems Applications | Artificial Intelligence | Formal Languages and Automata Theory | Database Management | Theory of ComputationAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 025.04 Online resources: Click here to access online
In:
Springer Nature eBookSummary: 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.
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.
There are no comments for this item.