Where to Position the Precision in Knowledge Extraction from Text

Authors:
Luo Xiao;Dieter Wissmann;Michael Brown;Stefan Jablonski
Affiliations:
-;-;-;-
Venue:
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Year:
2001

Citing 2
Cited 1

Information extraction as a basis for high-precision text classification

ACM Transactions on Information Systems (TOIS)
Learning Information Extraction Rules for Semi-Structured and Free Text

Machine Learning - Special issue on natural language learning

Information Extraction from the Web: System and Techniques

Applied Intelligence

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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.