152 lines
6.0 KiB
Java
152 lines
6.0 KiB
Java
package com.baeldung.opennlp;
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import opennlp.tools.chunker.ChunkerME;
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import opennlp.tools.chunker.ChunkerModel;
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import opennlp.tools.cmdline.postag.POSModelLoader;
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import opennlp.tools.doccat.DoccatFactory;
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import opennlp.tools.doccat.DoccatModel;
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import opennlp.tools.doccat.DocumentCategorizerME;
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import opennlp.tools.doccat.DocumentSample;
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import opennlp.tools.doccat.DocumentSampleStream;
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import opennlp.tools.namefind.NameFinderME;
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import opennlp.tools.namefind.TokenNameFinderModel;
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import opennlp.tools.postag.POSModel;
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import opennlp.tools.postag.POSSample;
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import opennlp.tools.postag.POSTaggerME;
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import opennlp.tools.sentdetect.SentenceDetectorME;
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import opennlp.tools.sentdetect.SentenceModel;
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import opennlp.tools.tokenize.WhitespaceTokenizer;
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import opennlp.tools.util.InputStreamFactory;
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import opennlp.tools.util.ObjectStream;
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import opennlp.tools.util.PlainTextByLineStream;
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import opennlp.tools.util.Span;
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import opennlp.tools.util.TrainingParameters;
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import org.junit.Test;
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import java.io.File;
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import java.io.FileInputStream;
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import java.io.FileNotFoundException;
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import java.io.IOException;
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import java.io.InputStream;
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import static org.junit.Assert.assertEquals;
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public class OpenNLPTests {
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private final static String text = "To get to the south: Go to the store. Buy a compass. Use the compass. Then walk to the south.";
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private final static String sentence[] = new String[]{"James", "Jordan", "live", "in", "Oklahoma", "city", "."};
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@Test
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public void givenText_WhenDetectSentences_ThenCountSentences() {
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InputStream is;
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SentenceModel model;
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try {
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is = new FileInputStream("OpenNLP/en-sent.bin");
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model = new SentenceModel(is);
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SentenceDetectorME sdetector = new SentenceDetectorME(model);
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String sentences[] = sdetector.sentDetect(text);
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assertEquals(4, sentences.length);
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is.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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@Test
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public void givenText_WhenDetectTokens_ThenVerifyNames() {
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InputStream is;
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TokenNameFinderModel model;
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try {
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is = new FileInputStream("OpenNLP/en-ner-person.bin");
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model = new TokenNameFinderModel(is);
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is.close();
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NameFinderME nameFinder = new NameFinderME(model);
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Span nameSpans[] = nameFinder.find(sentence);
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String[] names = Span.spansToStrings(nameSpans, sentence);
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assertEquals(1, names.length);
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assertEquals("James Jordan", names[0]);
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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@Test
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public void givenText_WhenDetectTokens_ThenVerifyLocations() {
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InputStream is;
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TokenNameFinderModel model;
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try {
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is = new FileInputStream("OpenNLP/en-ner-location.bin");
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model = new TokenNameFinderModel(is);
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is.close();
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NameFinderME nameFinder = new NameFinderME(model);
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Span locationSpans[] = nameFinder.find(sentence);
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String[] locations = Span.spansToStrings(locationSpans, sentence);
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assertEquals(1, locations.length);
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assertEquals("Oklahoma", locations[0]);
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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@Test
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public void givenText_WhenCategorizeDocument_ThenVerifyDocumentContent() {
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DoccatModel docCatModel;
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try {
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InputStreamFactory isf = new InputStreamFactory() {
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public InputStream createInputStream() throws IOException {
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return new FileInputStream("OpenNLP/doc-cat.train");
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}
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};
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ObjectStream<String> lineStream = new PlainTextByLineStream(isf, "UTF-8");
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ObjectStream<DocumentSample> sampleStream = new DocumentSampleStream(lineStream);
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DoccatFactory docCatFactory = new DoccatFactory();
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docCatModel = DocumentCategorizerME.train("en", sampleStream, TrainingParameters.defaultParams(), docCatFactory);
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DocumentCategorizerME myCategorizer = new DocumentCategorizerME(docCatModel);
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double[] outcomes = myCategorizer.categorize(sentence);
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String category = myCategorizer.getBestCategory(outcomes);
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assertEquals("GOOD", category);
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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@Test
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public void givenText_WhenTagDocument_ThenVerifyTaggedString() {
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try {
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POSModel posModel = new POSModelLoader().load(new File("OpenNLP/en-pos-maxent.bin"));
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POSTaggerME posTaggerME = new POSTaggerME(posModel);
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InputStreamFactory isf = new InputStreamFactory() {
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public InputStream createInputStream() throws IOException {
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return new FileInputStream("OpenNLP/PartOfSpeechTag.txt");
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}
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};
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ObjectStream<String> lineStream = new PlainTextByLineStream(isf, "UTF-8");
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String line;
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while ((line = lineStream.read()) != null) {
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String whitespaceTokenizerLine[] = WhitespaceTokenizer.INSTANCE.tokenize(line);
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String[] tags = posTaggerME.tag(whitespaceTokenizerLine);
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POSSample posSample = new POSSample(whitespaceTokenizerLine, tags);
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assertEquals("Out_IN of_IN the_DT night_NN that_WDT covers_VBZ me_PRP", posSample.toString());
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}
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lineStream.close();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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@Test
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public void givenText_WhenChunked_ThenCountChunks() {
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try {
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InputStream is = new FileInputStream("OpenNLP/en-chunker.bin");
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ChunkerModel cModel = new ChunkerModel(is);
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ChunkerME chunkerME = new ChunkerME(cModel);
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String pos[] = new String[]{"NNP", "NNP", "NNP", "POS", "NNP", "NN", "VBD"};
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String chunks[] = chunkerME.chunk(sentence, pos);
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assertEquals(7, chunks.length);
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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}
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