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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Ninomiya, T., Tsujii, J., & Miyao, Y. (2005). A persistent feature-object database for intelligent text archive systems. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3248, pp. 197–205). Springer Verlag. https://doi.org/10.1007/978-3-540-30211-7_21
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