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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Revision as of 10:13, 29 January 2013
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 precision, recall, and F-measure) 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 and f-measure 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.
