@Test
public void testTag() {
String sentence = "The driver got badly injured .";
String[] tokens = {"The", "driver", "got", "badly", "injured", "."};
String[] expecteds = {"DT", "NN", "VBD", "RB", "VBN", "."};
// openNLP.createPosTagger(OpenNLP.DEFAUL_POS_MODEL);
// openNLP.createTokenizer(OpenNLP.DEFAUL_TOKEN_MODEL);
String[] actuals1 = openNLP.tag(sentence);
String[] actuals2 = openNLP.tag(tokens);
assertArrayEquals(expecteds, actuals1);
assertArrayEquals(expecteds, actuals2);
}
@Test
public void testSentDetect() {
// openNLP.createSentenceDetector(OpenNLP.DEFAUL_SENT_MODEL);
String sampleSentences1 = "This is a test. There are many tests, this is the second.";
String[] sents = openNLP.sentDetect(sampleSentences1);
assertEquals(sents.length, 2);
assertEquals(sents[0], "This is a test.");
assertEquals(sents[1], "There are many tests, this is the second.");
}
@Test
public void testTokenize() {
String sentence = "The driver got badly injured.";
String[] expecteds = {"The", "driver", "got", "badly", "injured", "."};
// openNLP.createTokenizer(OpenNLP.DEFAUL_TOKEN_MODEL);
String actuals[] = openNLP.tokenize(sentence);
assertArrayEquals(expecteds, actuals);
}
@Test
public void testParse() {
String sentence =
"She was just another freighter from the States , and she seemed as commonplace as her name . ";
String expected =
"(TOP (S (S (NP (PRP She)) (VP (VBD was) (ADVP (RB just)) (NP (NP (DT another) (NN freighter)) (PP (IN from) (NP (DT the) (NNP States)))))) (, ,) (CC and) (S (NP (PRP she)) (VP (VBD seemed) (ADJP (ADJP (RB as) (JJ commonplace)) (PP (IN as) (NP (PRP$ her) (NN name)))))) (. .)))";
// openNLP.createParser(OpenNLP.DEFAUL_PARSER_MODEL);
Parse parse = openNLP.parse(sentence);
StringBuffer parseString = new StringBuffer();
parse.show(parseString);
String actual = parseString.toString();
System.out.println("@testParse actual:" + actual);
assertEquals(expected, actual);
}
@Test
public void testChunk() {
String[] tokens = {
"Rockwell",
"said",
"the",
"agreement",
"calls",
"for",
"it",
"to",
"supply",
"200",
"additional",
"so-called",
"shipsets",
"for",
"the",
"planes",
"."
};
String[] tags = {
"NNP", "VBD", "DT", "NN", "VBZ", "IN", "PRP", "TO", "VB", "CD", "JJ", "JJ", "NNS", "IN", "DT",
"NNS", "."
};
String[] expecteds = {
"B-NP", "B-VP", "B-NP", "I-NP", "B-VP", "B-SBAR", "B-NP", "B-VP", "I-VP", "B-NP", "I-NP",
"I-NP", "I-NP", "B-PP", "B-NP", "I-NP", "O"
};
// openNLP.createChunker(OpenNLP.DEFAUL_CHUNKER_MODEL);
String[] actuals = openNLP.chunk(tokens, tags);
assertArrayEquals(expecteds, actuals);
}
/**
* Tests for the {@link OpenNLP} class.
*
* @author bin3 ([email protected])
* @version 0.1.0
* @date 2011-5-15
*/
public class OpenNLPTest {
private OpenNLP openNLP = OpenNLP.getInstance();
/**
* TODO
*
* @throws java.lang.Exception
*/
@Before
public void setUp() throws Exception {}
/**
* TODO
*
* @throws java.lang.Exception
*/
@After
public void tearDown() throws Exception {}
@Test
public void testParse() {
String sentence =
"She was just another freighter from the States , and she seemed as commonplace as her name . ";
String expected =
"(TOP (S (S (NP (PRP She)) (VP (VBD was) (ADVP (RB just)) (NP (NP (DT another) (NN freighter)) (PP (IN from) (NP (DT the) (NNP States)))))) (, ,) (CC and) (S (NP (PRP she)) (VP (VBD seemed) (ADJP (ADJP (RB as) (JJ commonplace)) (PP (IN as) (NP (PRP$ her) (NN name)))))) (. .)))";
// openNLP.createParser(OpenNLP.DEFAUL_PARSER_MODEL);
Parse parse = openNLP.parse(sentence);
StringBuffer parseString = new StringBuffer();
parse.show(parseString);
String actual = parseString.toString();
System.out.println("@testParse actual:" + actual);
assertEquals(expected, actual);
}
@Test
public void testSentDetect() {
// openNLP.createSentenceDetector(OpenNLP.DEFAUL_SENT_MODEL);
String sampleSentences1 = "This is a test. There are many tests, this is the second.";
String[] sents = openNLP.sentDetect(sampleSentences1);
assertEquals(sents.length, 2);
assertEquals(sents[0], "This is a test.");
assertEquals(sents[1], "There are many tests, this is the second.");
}
@Test
public void testTokenize() {
String sentence = "The driver got badly injured.";
String[] expecteds = {"The", "driver", "got", "badly", "injured", "."};
// openNLP.createTokenizer(OpenNLP.DEFAUL_TOKEN_MODEL);
String actuals[] = openNLP.tokenize(sentence);
assertArrayEquals(expecteds, actuals);
}
@Test
public void testTag() {
String sentence = "The driver got badly injured .";
String[] tokens = {"The", "driver", "got", "badly", "injured", "."};
String[] expecteds = {"DT", "NN", "VBD", "RB", "VBN", "."};
// openNLP.createPosTagger(OpenNLP.DEFAUL_POS_MODEL);
// openNLP.createTokenizer(OpenNLP.DEFAUL_TOKEN_MODEL);
String[] actuals1 = openNLP.tag(sentence);
String[] actuals2 = openNLP.tag(tokens);
assertArrayEquals(expecteds, actuals1);
assertArrayEquals(expecteds, actuals2);
}
@Test
public void testChunk() {
String[] tokens = {
"Rockwell",
"said",
"the",
"agreement",
"calls",
"for",
"it",
"to",
"supply",
"200",
"additional",
"so-called",
"shipsets",
"for",
"the",
"planes",
"."
};
String[] tags = {
"NNP", "VBD", "DT", "NN", "VBZ", "IN", "PRP", "TO", "VB", "CD", "JJ", "JJ", "NNS", "IN", "DT",
"NNS", "."
};
String[] expecteds = {
"B-NP", "B-VP", "B-NP", "I-NP", "B-VP", "B-SBAR", "B-NP", "B-VP", "I-VP", "B-NP", "I-NP",
"I-NP", "I-NP", "B-PP", "B-NP", "I-NP", "O"
};
// openNLP.createChunker(OpenNLP.DEFAUL_CHUNKER_MODEL);
String[] actuals = openNLP.chunk(tokens, tags);
assertArrayEquals(expecteds, actuals);
}
}
