/*
* 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.chunker;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import opennlp.tools.util.FilterObjectStream;
import opennlp.tools.util.ObjectStream;
/**
* This dummy chunk sample stream reads a file formatted as described at
* <a hraf="http://www.cnts.ua.ac.be/conll2000/chunking/output.html/">] and
* can be used together with DummyChunker simulate a chunker.
*/
public class DummyChunkSampleStream extends
FilterObjectStream<String, ChunkSample> {
private boolean mIsPredicted;
private int count = 0;
// the predicted flag sets if the stream will contain the expected or the
// predicted tags.
public DummyChunkSampleStream(ObjectStream<String> samples, boolean isPredicted) {
super(samples);
mIsPredicted = isPredicted;
}
/**
* Returns a pair representing the expected and the predicted at 0: the
* chunk tag according to the corpus at 1: the chunk tag predicted
*
* @see opennlp.tools.util.ObjectStream#read()
*/
public ChunkSample read() throws IOException {
List<String> toks = new ArrayList<>();
List<String> posTags = new ArrayList<>();
List<String> chunkTags = new ArrayList<>();
List<String> predictedChunkTags = new ArrayList<>();
for (String line = samples.read(); line != null && !line.equals(""); line = samples
.read()) {
String[] parts = line.split(" ");
if (parts.length != 4) {
System.err.println("Skipping corrupt line " + count + ": "
+ line);
} else {
toks.add(parts[0]);
posTags.add(parts[1]);
chunkTags.add(parts[2]);
predictedChunkTags.add(parts[3]);
}
count++;
}
if (toks.size() > 0) {
if (mIsPredicted) {
return new ChunkSample(toks.toArray(new String[toks.size()]),
posTags.toArray(new String[posTags.size()]),
predictedChunkTags
.toArray(new String[predictedChunkTags.size()]));
} else
return new ChunkSample(toks.toArray(new String[toks.size()]),
posTags.toArray(new String[posTags.size()]),
chunkTags.toArray(new String[chunkTags.size()]));
} else {
return null;
}
}
}