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本文主要研究下如何使用opennlp进行词性标注
POS Tagging
词性（Part of Speech, POS），标注是对一个词汇或一段文字进行描述的过程。这个描述被称为一个标注。
目前流行的中文词性标签有两大类：北大词性标注集和宾州词性标注集。现代汉语的词可以分为两类12种词性：一类是实词：名词、动词、形容词、数词、量词和代词；另一类是虚词：副词、介词、连词、助词、叹词和拟声词。
这块的技术大多数使用HMM（隐马尔科夫模型）+ Viterbi算法，最大熵算法（Maximum Entropy）。
OpenNLP里头可以使用POSTaggerME类来执行基本的标注，以及ChunkerME类来执行分块。
POSTaggerME
public static POSModel trainPOSModel(ModelType type) throws IOException {
TrainingParameters params = new TrainingParameters();
params.put(TrainingParameters.ALGORITHM_PARAM, type.toString());
params.put(TrainingParameters.ITERATIONS_PARAM, 100);
params.put(TrainingParameters.CUTOFF_PARAM, 5);
return POSTaggerME.train("eng", createSampleStream(), params,
new POSTaggerFactory());
}
private static ObjectStream createSampleStream() throws IOException {
InputStreamFactory in = new ResourceAsStreamFactory(POSTaggerMETest.class,
"postag/AnnotatedSentences.txt");
return new WordTagSampleStream(new PlainTextByLineStream(in, StandardCharsets.UTF_8));
}
@Test
public void testPOSTagger() throws IOException {
POSModel posModel = trainPOSModel(ModelType.MAXENT);
POSTagger tagger = new POSTaggerME(posModel);
String[] tags = tagger.tag(new String[] {
"The",
"driver",
"got",
"badly",
"injured",
"."});
Assert.assertEquals(6, tags.length);
Assert.assertEquals("DT", tags[0]);
Assert.assertEquals("NN", tags[1]);
Assert.assertEquals("VBD", tags[2]);
Assert.assertEquals("RB", tags[3]);
Assert.assertEquals("VBN", tags[4]);
Assert.assertEquals(".", tags[5]);
} 这里首先进行模型训练，其中训练文本样式如下：
Last_JJ September_NNP ,_, I_PRP tried_VBD to_TO find_VB out_RP the_DT address_NN of_IN an_DT old_JJ school_NN friend_NN whom_WP I_PRP had_VBD not_RB seen_VBN for_IN 15_CD years_NNS ._.
I_PRP just_RB knew_VBD his_PRP$ name_NN ,_, Alan_NNP McKennedy_NNP ,_, and_CC I_PRP 'd_MD heard_VBD the_DT rumour_NN that_IN he_PRP 'd_MD moved_VBD to_TO Scotland_NNP ,_, the_DT country_NN of_IN his_PRP$ ancestors_NNS ._.
So_IN I_PRP called_VBD Julie_NNP ,_, a_DT friend_NN who's_WDT still_RB in_IN contact_NN with_IN him_PRP ._.
She_PRP told_VBD me_PRP that_IN he_PRP lived_VBD in_IN 23213_CD Edinburgh_NNP ,_, Worcesterstreet_NNP 12_CD ._.
I_PRP wrote_VBD him_PRP a_DT letter_NN right_RB away_RB and_CC he_PRP answered_VBD soon_RB ,_, sounding_VBG very_RB happy_JJ and_CC delighted_JJ ._.标注说明：
- DT(
Determiner) - NN (
Noun, singular or mass) - VBD (
Verb, past tense) - RB (
Adverb) - VBN (
Verb, past participle)
ChunkerME
private Chunker chunker;
private static String[] toks1 = { "Rockwell", "said", "the", "agreement", "calls", "for",
"it", "to", "supply", "200", "additional", "so-called", "shipsets",
"for", "the", "planes", "." };
private static String[] tags1 = { "NNP", "VBD", "DT", "NN", "VBZ", "IN", "PRP", "TO", "VB",
"CD", "JJ", "JJ", "NNS", "IN", "DT", "NNS", "." };
private static String[] expect1 = { "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" };
@Before
public void startup() throws IOException {
ResourceAsStreamFactory in = new ResourceAsStreamFactory(getClass(),
"chunker/test.txt");
ObjectStream sampleStream = new ChunkSampleStream(
new PlainTextByLineStream(in, StandardCharsets.UTF_8));
TrainingParameters params = new TrainingParameters();
params.put(TrainingParameters.ITERATIONS_PARAM, 70);
params.put(TrainingParameters.CUTOFF_PARAM, 1);
ChunkerModel chunkerModel = ChunkerME.train("eng", sampleStream, params, new ChunkerFactory());
this.chunker = new ChunkerME(chunkerModel);
}
@Test
public void testChunkAsArray() throws Exception {
String[] preds = chunker.chunk(toks1, tags1);
Assert.assertArrayEquals(expect1, preds);
} 这里同样也进行了模型训练，其训练文本样式如下：
Rockwell NNP B-NP
International NNP I-NP
Corp. NNP I-NP
's POS B-NP
Tulsa NNP I-NP
unit NN I-NP
said VBD B-VP
it PRP B-NP
signed VBD B-VP
a DT B-NP
tentative JJ I-NP
agreement NN I-NP
extending VBG B-VP
its PRP$ B-NP
contract NN I-NP
with IN B-PP
Boeing NNP B-NP
Co. NNP I-NP
to TO B-VP
provide VB I-VP
structural JJ B-NP
parts NNS I-NP
for IN B-PP
Boeing NNP B-NP
's POS B-NP
747 CD I-NP
jetliners NNS I-NP标注说明：
- B 标注开始
- I 标注的中间
- E 标注的结束
- NP 名词块
- VB 动词块
小结
本文初步展示了如何使用opennlp进行词性标注，模型训练是个比较重要的一个方面，可以通过特定训练提高特定领域文本的标注准确性。
doc
- NLP汉语自然语言处理原理与实践
- Penn Part of Speech Tags