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