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An Overview and Classification of Adaptive Approaches to Information Extraction

  • Christian Siefkes
  • Peter Siniakov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3730)

Abstract

Most of the information stored in digital form is hidden in natural language texts. Extracting and storing it in a formal representation (e.g. in form of relations in databases) allows efficient querying, easy administration and further automatic processing of the extracted data. The area of information extraction (IE) comprises techniques, algorithms and methods performing two important tasks: finding (identifying) the desired, relevant data and storing it in appropriate form for future use.

The rapidly increasing number and diversity of IE systems are the evidence of continuous activity and growing attention to this field. At the same time it is becoming more and more difficult to overview the scope of IE, to see advantages of certain approaches and differences to others. In this paper we identify and describe promising approaches to IE. Our focus is adaptive systems that can be customized for new domains through training or the use of external knowledge sources. Based on the observed origins and requirements of the examined IE techniques a classification of different types of adaptive IE systems is established.

Keywords

Information Extraction Target Structure Adaptive Approach Horn Clause Prepositional Phrase 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christian Siefkes
    • 1
    • 2
  • Peter Siniakov
    • 1
  1. 1.Database and Information Systems GroupFreie Universität BerlinBerlinGermany
  2. 2.Berlin-Brandenburg Graduate School in Distributed Information Systems 

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