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Add an Eval which checks the accuracy of the top layer
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package edu.stanford.nlp.parser.metrics; | ||
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import java.util.Collections; | ||
import java.util.Set; | ||
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import edu.stanford.nlp.trees.Constituent; | ||
import edu.stanford.nlp.trees.ConstituentFactory; | ||
import edu.stanford.nlp.trees.LabeledScoredConstituentFactory; | ||
import edu.stanford.nlp.trees.Tree; | ||
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/** | ||
* Measures accuracy by only considering the very top of the parse tree, eg where S, SINV, etc go | ||
* | ||
* @author John Bauer | ||
*/ | ||
public class TopMatchEval extends AbstractEval { | ||
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private final ConstituentFactory cf; | ||
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public TopMatchEval(String name, boolean runningAverages) { | ||
super(name, runningAverages); | ||
cf = new LabeledScoredConstituentFactory(); | ||
} | ||
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@Override | ||
protected Set<Constituent> makeObjects(Tree tree) { | ||
if (tree == null) { | ||
return Collections.emptySet(); | ||
} | ||
// The eval trees won't have a root level, instead starting with | ||
// the S/SINV/FRAG/whatever, so just eval at the top level | ||
Set<Constituent> result = tree.constituents(cf, 0); | ||
return result; | ||
} | ||
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} | ||
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