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Introduction

  • Henning WachsmuthEmail author
Chapter
  • 1.5k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9383)

Abstract

The future of information search is not browsing through tons of web pages or documents. In times of big data and the information overload of the internet, experts in the field agree that both everyday and enterprise search will gradually shift from only retrieving large numbers of texts that potentially contain relevant information to directly mining relevant information in these texts (Etzioni 2011; Kelly and Hamm 2013; Ananiadou et al. 2013). In this chapter, we first motivate the benefit of such large-scale text mining for today’s web search and big data analytics applications (Sect. 1.1). Then, we reveal the task specificity and the process complexity of analyzing natural language text as the main problems that prevent applications from performing text mining ad-hoc , i.e., immediately in response to a user query (Sect. 1.2). Section 1.3 points out how we propose to tackle these problems by improving the design , efficiency , and domain robustness of the pipelines of algorithms used for text analysis with artificial intelligence techniques. This leads to the contributions of the book at hand (Sect. 1.4).

Keywords

Text Analysis Text Mining Input Text Central Research Question Gate Gate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Bauhaus-Universität WeimarWeimarGermany

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