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.

Authors and Affiliations

Bauhaus-Universität, Weimar, Germany

Henning Wachsmuth

Bibliographic Information

Book Title: Text Analysis Pipelines
Book Subtitle: Towards Ad-hoc Large-Scale Text Mining
Authors: Henning Wachsmuth
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-25741-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Softcover ISBN: 978-3-319-25740-2Published: 04 December 2015
eBook ISBN: 978-3-319-25741-9Published: 02 December 2015
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XX, 302
Number of Illustrations: 74 illustrations in colour
Topics: Information Storage and Retrieval, Information Systems Applications (incl. Internet), Artificial Intelligence, Mathematical Logic and Formal Languages, Database Management, Computation by Abstract Devices

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Text Analysis Pipelines

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Table of contents (6 chapters)

Front Matter

Introduction