Text Analysis Pipelines

Towards Ad-hoc Large-Scale Text Mining

  • Henning Wachsmuth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9383)

Table of contents

  1. Front Matter
    Pages I-XX
  2. Henning Wachsmuth
    Pages 1-17
  3. Henning Wachsmuth
    Pages 19-53
  4. Henning Wachsmuth
    Pages 55-121
  5. Henning Wachsmuth
    Pages 123-182
  6. Henning Wachsmuth
    Pages 183-230
  7. Henning Wachsmuth
    Pages 231-238
  8. Back Matter
    Pages 239-302

About this book

Introduction

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.


Keywords

Efficienciy and effectiveness Information extraction Machine learning Natural language processing Text minig Adaptive scheduling Argumentation structure Artificial intelligence Big data Domain robustness Information structure Language function analysis Online learning Parallelization Pipeline Pipeline design Sentiment analysis Similarity Text analysis Text classification

Authors and affiliations

  • Henning Wachsmuth
    • 1
  1. 1.Bauhaus-UniversitätWeimarGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-25741-9
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-25740-2
  • Online ISBN 978-3-319-25741-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book