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Computing precision and recall metrics for named entity tagging and resolution involves classifying text spans as true positives, false positives, or false negatives. There are many factors that make this classification complicated for real world systems. We describe an evaluation system that attempts to control this complexity through a set of rules and a forward chaining inference engine.

Increasing maintainability of NLP evaluation modules through declarative implementations