细节化 OpenNLP

6 细节化

功能介绍：文本分块由除以单词句法相关部分，如名词基，动词基的文字，但没有指定其内部结构，也没有其在主句作用。

API：该概括化提供了一个API来培养新的概括化的模式。下面的示例代码演示了如何做到这一点：

 

测试代码

package package01;

import opennlp.tools.chunker.ChunkerME;
import opennlp.tools.chunker.ChunkerModel;
import opennlp.tools.cmdline.postag.POSModelLoader;
import opennlp.tools.postag.POSModel;
import opennlp.tools.postag.POSSample;
import opennlp.tools.postag.POSTaggerME;
import opennlp.tools.tokenize.WhitespaceTokenizer;
import opennlp.tools.util.*;

import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.nio.charset.Charset;

public class Test06 {

    public static void main(String[] args) throws IOException {
        Test06.chunk();
    }

    /**
     * 5.序列标注:Chunker
     * @deprecated 通过使用标记生成器生成的tokens分为一个句子划分为一组块。What chunker does is to partition a sentence to a set of chunks by using the tokens generated by tokenizer.
     *
     * 输入值
     * Hi. How are you? This is Mike.
     */
    public static void chunk() throws IOException {
        POSModel model = new POSModelLoader().load(new File("E:\\NLP_Practics\\models\\en-pos-maxent.bin"));
        //PerformanceMonitor perfMon = new PerformanceMonitor(System.err, "sent");
        POSTaggerME tagger = new POSTaggerME(model);
//        ObjectStream<String> lineStream = new PlainTextByLineStream(new StringReader(str));

        Charset charset = Charset.forName("UTF-8");
        InputStreamFactory isf = new MarkableFileInputStreamFactory(new File("E:\\myText.txt"));
        ObjectStream<String> lineStream = new PlainTextByLineStream(isf, charset);

        //perfMon.start();
        String line;
        String whitespaceTokenizerLine[] = null;
        String[] tags = null;
        while ((line = lineStream.read()) != null) {
            whitespaceTokenizerLine = WhitespaceTokenizer.INSTANCE.tokenize(line);
            tags = tagger.tag(whitespaceTokenizerLine);
            POSSample sample = new POSSample(whitespaceTokenizerLine, tags);
            System.out.println(sample.toString());
            //perfMon.incrementCounter();
        }
        //perfMon.stopAndPrintFinalResult();

        // chunker
        InputStream is = new FileInputStream("E:\\NLP_Practics\\models\\en-chunker.bin");
        ChunkerModel cModel = new ChunkerModel(is);
        ChunkerME chunkerME = new ChunkerME(cModel);
        String result[] = chunkerME.chunk(whitespaceTokenizerLine, tags);
        for (String s : result)
            System.out.println(s);
        Span[] span = chunkerME.chunkAsSpans(whitespaceTokenizerLine, tags);
        for (Span s : span)
            System.out.println(s.toString());
        System.out.println("--------------5-------------");
        is.close();
    }
}

　　

 

结果

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Loading POS Tagger model ... done (0.554s) Hi._NNP How_WRB are_VBP you?_JJ This_DT is_VBZ Mike._NNP B-NP B-ADVP O B-NP I-NP B-VP O [0..1) NP [1..2) ADVP [3..5) NP [5..6) VP --------------5-------------