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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Ninomiya, T., Tsujii, J., Miyao, Y. (2005). A Persistent Feature-Object Database for Intelligent Text Archive Systems. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_21
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DOI: https://doi.org/10.1007/978-3-540-30211-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24475-2
Online ISBN: 978-3-540-30211-7
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