opennlp.tools.doccat.NGramFeatureGenerator Maven / Gradle / Ivy
/*
* 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.doccat;
import opennlp.tools.util.InvalidFormatException;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Map;
/**
* Generates ngram features for a document.
* n-gram {@link FeatureGenerator}
*/
public class NGramFeatureGenerator implements FeatureGenerator {
//default values for bigrams
private int minGram = 2;
private int maxGram = 2;
/**
* Constructor for ngrams.
*
* @param minGram minGram value - which means minimum words in ngram features
* @param maxGram maxGram value - which means maximum words in ngram features
* @throws InvalidFormatException
*/
public NGramFeatureGenerator(int minGram, int maxGram) throws InvalidFormatException {
if (minGram > 0 && maxGram > 0) {
if (minGram <= maxGram) {
this.minGram = minGram;
this.maxGram = maxGram;
} else {
throw new InvalidFormatException("Minimum range value (minGram) should be less than or equal to maximum range value (maxGram)!");
}
} else {
throw new InvalidFormatException("Both minimum range value (minGram) & maximum range value (maxGram) should be greater than or equal to 1!");
}
}
/**
* Default constructor for Bi grams
*/
public NGramFeatureGenerator() {
}
/**
* Extract ngram features from given text fragments
*
* @param text the text fragments to extract features from
* @param extraInfo optional extra information
* @return a collection of n gram features
*/
public Collection extractFeatures(String[] text, Map extraInfo) {
List features = new ArrayList<>();
for (int i = 0; i <= text.length - minGram; i++) {
String feature = "ng=";
for (int y = 0; y < maxGram && i + y < text.length; y++) {
feature = feature + ":" + text[i + y];
int gramCount = y + 1;
if (maxGram >= gramCount && gramCount >= minGram) {
features.add(feature);
}
}
}
return features;
}
}