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A Persistent Feature-Object Database for Intelligent Text Archive Systems

  • Takashi Ninomiya
  • Jun’ichi Tsujii
  • Yusuke Miyao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

This paper describes an intelligent text archive system in which typed feature structures are embedded. The aim of the system is to associate feature structures with regions in text, to make indexes for efficient retrieval, to allow users to specify both structure and proximity, and to enable inference on typed feature structures embedded in text. We propose a persistent mechanism for storing typed feature structures and the architecture of the text archive system.

Keywords

Inference Engine Feature Object Partial Loading Indexing Scheme Type Constraint 
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

  • Takashi Ninomiya
    • 1
    • 2
  • Jun’ichi Tsujii
    • 1
    • 2
  • Yusuke Miyao
    • 2
  1. 1.CREST, Japan Science and Technology AgencyKawaguchi Center BuildingSaitama
  2. 2.Department of Computer ScienceUniversity of TokyoTokyo

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