opennlp.tools.lemmatizer.LemmatizerEvaluator Maven / Gradle / Ivy
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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opennlp.tools.lemmatizer;
import opennlp.tools.util.eval.Evaluator;
import opennlp.tools.util.eval.Mean;
/**
* The {@link LemmatizerEvaluator} measures the performance of
* the given {@link Lemmatizer} with the provided reference
* {@link LemmaSample}s.
*/
public class LemmatizerEvaluator extends Evaluator {
private Lemmatizer lemmatizer;
private Mean wordAccuracy = new Mean();
/**
* Initializes the current instance.
*
* @param aLemmatizer a lemmatizer
* @param listeners an array of evaluation listeners
*/
public LemmatizerEvaluator(Lemmatizer aLemmatizer, LemmatizerEvaluationMonitor ... listeners) {
super(listeners);
this.lemmatizer = aLemmatizer;
}
/**
* Evaluates the given reference {@link LemmaSample} object.
*
* This is done by tagging the sentence from the reference
* {@link LemmaSample} with the {@link Lemmatizer}. The
* tags are then used to update the word accuracy score.
*
* @param reference the reference {@link LemmaSample}.
*
* @return the predicted {@link LemmaSample}.
*/
@Override
protected LemmaSample processSample(LemmaSample reference) {
String[] predictedLemmas = lemmatizer.lemmatize(reference.getTokens(), reference.getTags());
String[] referenceLemmas = reference.getLemmas();
for (int i = 0; i < referenceLemmas.length; i++) {
if (referenceLemmas[i].equals(predictedLemmas[i])) {
wordAccuracy.add(1);
}
else {
wordAccuracy.add(0);
}
}
return new LemmaSample(reference.getTokens(), reference.getTags(), predictedLemmas);
}
/**
* Retrieves the word accuracy.
*
* This is defined as:
* word accuracy = correctly detected tags / total words
*
* @return the word accuracy
*/
public double getWordAccuracy() {
return wordAccuracy.mean();
}
/**
* Retrieves the total number of words considered
* in the evaluation.
*
* @return the word count
*/
public long getWordCount() {
return wordAccuracy.count();
}
/**
* Represents this objects as human readable {@link String}.
*/
@Override
public String toString() {
return "Accuracy:" + wordAccuracy.mean() +
" Number of Samples: " + wordAccuracy.count();
}
}