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Text Analysis Pipelines : Towards Ad-hoc Large-Scale Text Mining / by Henning Wachsmuth.
- Format:
- Book
- Author/Creator:
- Wachsmuth, Henning, Author.
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 9383
- Theoretical Computer Science and General Issues, 2512-2029 ; 9383
- Language:
- English
- Subjects (All):
- 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 Computation.
- Local Subjects:
- Information Storage and Retrieval.
- Computer and Information Systems Applications.
- Artificial Intelligence.
- Formal Languages and Automata Theory.
- Database Management.
- Theory of Computation.
- Physical Description:
- 1 online resource (XX, 302 pages) : 74 illustrations in color.
- Edition:
- 1st ed. 2015.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2015.
- System Details:
- text file PDF
- Summary:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-25741-9
- 9783319257419
- Access Restriction:
- Restricted for use by site license.
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