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.
Cite
CITATION STYLE
Xiao, L., Wissmann, D., Brown, M., & Jablonski, S. (2001). Where to position the precision in knowledge extraction from text. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2070, pp. 187–196). Springer Verlag. https://doi.org/10.1007/3-540-45517-5_22
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