Abstract
This paper concerns knowledge extraction for applications concerning the automated filling of templates from an input of semi-structured textual documents. The template filling task can be viewed as a collaboration between a number of agents, including NE-Agents that are specialised to detect occurrences of specific features in the text and TE-Agents that specialise at combining the results from multiple NE-Agents in order to create a template instance. This paper presents an automated learning approach for the generation of a TE-Agent that extracts spatial relationships between the various features of a template. It is shown that this TE-Agent can compensate for imprecise performance on the part of the NE-Agents.
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© 2001 Springer-Verlag Berlin Heidelberg
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Xiao, L., Wissmann, D., Brown, M., Jablonski, S. (2001). Where to Position the Precision in Knowledge Extraction from Text. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_22
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DOI: https://doi.org/10.1007/3-540-45517-5_22
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