KnowledgeExtractionToolEval

From STLab

A landscape analysis of some knowledge extraction tools

This page includes the complete results for a landscape analysis of Knowledge Extraction (KE) tools.

A technical report about the analysis (including only some of the results) can be downloaded here.

The table shows, for each basic NLP task corresponding to a basic knowledge engineering task (e.g. sense tagging in NLP roughly maps to entity typing in KE), the absolute and normalized results (in terms of P(recision), R(ecall(, F(-measure), A(ccuracy)) obtained by each KE tool.

The Merging column contains the results when all outputs are merged. The merged results are also used as the upper limit for evaluating the recall, f-measure, and accuracy of the individual tools.

Values in red are better than the average by one std; values in green are better than the average by .5 std.

The results for semantic role labelling and frame detection include the combined results for: frames; role-filler pairs; role labels; filler targets. See a dedicated table for analytic results on this basic task.