Information Extraction Grammars

  • Mónica Marrero
  • Julián Urbano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)


Formal grammars are extensively used to represent patterns in Information Extraction, but they do not permit the use of several types of features. Finite-state transducers, which are based on regular grammars, solve this issue, but they have other disadvantages such as the lack of expressiveness and the rigid matching priority. As an alternative, we propose Information Extraction Grammars. This model, supported on Language Theory, does permit the use of several features, solves some of the problems of finite-state transducers, and has the same computational complexity in recognition as formal grammars, whether they describe regular or context-free languages.


Regular Expression Information Extraction Parse Tree Name Entity Recognition Input String 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Mónica Marrero
    • 1
  • Julián Urbano
    • 2
  1. 1.Barcelona Supercomputing CenterSpain
  2. 2.Universitat Pompeu FabraBarcelonaSpain

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