Optimizing Selection of Assessment Solutions for Completing Information Extraction Results

Authors

  • Christina Feilmayr Johannes Kepler University Linz, Institute of Application Oriented Knowledge Processing

DOI:

https://doi.org/10.13053/cys-17-2-1521

Keywords:

Information extraction, information quality, method selection, data and text mining.

Abstract

Incomplete information produces serious consequences in information extraction: it increases costs and leads to problems in downstream processing. This work focuses on improving the completeness of extraction results by applying judiciously selected assessment methods to information extraction based on the principle of complementarity. Our recommendation model simplifies the selection of assessment methods which can overcome a specific incompleteness problem. This paper also focuses on the characterization of information extraction and assessment methods as well as on a rule-based approach that allows estimation of general processability, profitability in the complementarity approach, and the performance of an assessment method under evaluation.

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Published

2013-06-29

Issue

Vol. 17 No. 2 (2013): 17(2) April-June 2013

Section

Articles

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