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.
