Java Parser类代码示例

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public static void main(String[] args) throws IOException {
    if (args.length == 0) {
        System.err.println("Usage: AnswerTypeEventStream eventfile");
        System.exit(1);
    }
    int ai = 0;
    String eventFile = args[ai++];
    String modelsDirProp = System.getProperty("models.dir", "book/src/main" + File.separator + "opennlp-models" +
            File.separator + "english");
    File modelsDir = new File(modelsDirProp);
    File wordnetDir = new File(System.getProperty("wordnet.dir", "book/src/main" + File.separator + "WordNet-3.0" + File.separator + "dict"));
    InputStream chunkerStream = new FileInputStream(
            new File(modelsDir, "en-chunker.bin"));
    ChunkerModel chunkerModel = new ChunkerModel(chunkerStream);
    ChunkerME chunker = new ChunkerME(chunkerModel);
    InputStream posStream = new FileInputStream(
            new File(modelsDir, "en-pos-maxent.bin"));
    POSModel posModel = new POSModel(posStream);
    POSTaggerME tagger = new POSTaggerME(posModel);
    Parser parser = new ChunkParser(chunker, tagger);
    AnswerTypeContextGenerator actg = new AnswerTypeContextGenerator(wordnetDir);
    EventStream es = new AnswerTypeEventStream(eventFile, actg, parser);
    while (es.hasNext()) {
        System.out.println(es.next().toString());
    }
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public double scoreStructure(String ca, String q, String passage, boolean verbose) throws InvalidFormatException, IOException{
	POSTaggerME parserModel = new POSTaggerME(new POSModel(new FileInputStream(new File("en-pos-model.bin"))));
	Tokenizer tokenizer = new TokenizerME(new TokenizerModel(new FileInputStream(new File("en-token.bin"))));
	Parser parser = ParserFactory.create(new ParserModel(new FileInputStream(new File("en-parser.bin"))));
	double score = 0;
	
	Parse[] questionParse = ParserTool.parseLine(q, parser, 1);
	Parse[] passageParse = ParserTool.parseLine(q, parser, 1);
	
	if (passage.contains(ca)) {
		for (int i =0; i < questionParse.length; i++) {
			score += matchChildren(questionParse[i],passageParse[i]);
		}
	}
	
	return score;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public void parserTest1() throws IOException {
	if (!this.modelsAreInitialized) init();
	Parser parser = ParserFactory.create(
			this.parserModel,
			20, // beam size
			0.95); 
	Parse[] results = ParserTool.parseLine("Jane Austen was very modest about her own genius ."+this.q,
			parser, 1);
	Parse[] qResults = ParserTool.parseLine(this.q,parser, 1);
	Parse[] rChn = (results[0].getChildren())[0].getChildren();
	
	results[0].expandTopNode(results[0]);
	for (int i = 0; i < results.length; i++) {
		results[i].show();
	}
	for (int i = 0; i < qResults.length; i++) {
		qResults[i].show();
	}
	System.out.print("\n\n");
	for (int i = 0; i < rChn.length; i++) {
		rChn[i].show();
		System.out.print("\n");
	}
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public static Parse[] parsePassageText(String p) throws InvalidFormatException{
	
	//initialize 	 
	SentenceDetectorME sentenceDetector = new SentenceDetectorME(sentenceModel);
	Parser parser = ParserFactory.create(
			parserModel,
			20, // beam size
			0.95); // advance percentage
 	 	 
	String[] sentences = sentenceDetector.sentDetect(p);
	Parse[] results = new Parse[sentences.length];
	for (int i=0;i<sentences.length;i++){
		String[] tks = SimpleTokenizer.INSTANCE.tokenize(sentences[i]);


		String sent= StringUtils.join(tks," ");
		System.out.println("Found sentence " + sent);
		Parse[] sentResults = ParserTool.parseLine(sent,parser, 1);
		results[i]=sentResults[0];
	}
	return results;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public Parse[] parsePassageText(String p) throws InvalidFormatException{
	if (!modelsAreInitialized)init();
	//initialize 	 
	SentenceDetectorME sentenceDetector = new SentenceDetectorME(this.sentenceModel);
	Parser parser = ParserFactory.create(
			this.parserModel,
			20, // beam size
			0.95); // advance percentage
	//find sentences, tokenize each, parse each, return top parse for each 	 	 
	String[] sentences = sentenceDetector.sentDetect(p);
	Parse[] results = new Parse[sentences.length];
	for (int i=0;i<sentences.length;i++){
		String[] tks = SimpleTokenizer.INSTANCE.tokenize(sentences[i]);
		//StringTokenizer st = new StringTokenizer(tks[i]); 
		//There are several tokenizers available. SimpleTokenizer works best

		String sent= StringUtils.join(tks," ");
		System.out.println("Found sentence " + sent);
		Parse[] sentResults = ParserTool.parseLine(sent,parser, 1);
		results[i]=sentResults[0];
	}
	return results;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
@Override
public QParser createParser(String qStr, SolrParams localParams, SolrParams params,
                            SolrQueryRequest req) {
  answerTypeMap = new HashMap<String, String>();//<co id="qqpp.atm"/>
  answerTypeMap.put("L", "NE_LOCATION");
  answerTypeMap.put("T", "NE_TIME|NE_DATE");
  answerTypeMap.put("P", "NE_PERSON");
  answerTypeMap.put("M", "NE_MONEY");
  answerTypeMap.put("O", "NE_ORGANIZATION");
  answerTypeMap.put("L", "NE_LOCATION");
  answerTypeMap.put("C", "NE_PERCENT");
  answerTypeMap.put("F", "DESCRIPTION");
  answerTypeMap.put("X", "OTHERS");
  QParser qParser;
  if (params.getBool(OWLParams.COMPONENT_NAME, false) == true //<co id="qqpp.explainif"/>
          && qStr.equals("*:*") == false) {
    AnswerTypeClassifier atc =
            new AnswerTypeClassifier(model, probs, atcg);//<co id="qqpp.atc"/>
    Parser parser = new ChunkParser(chunker, tagger);//<co id="qqpp.parser"/>
    qParser = new WiseOwlQParser(qStr, localParams, //<co id="qqpp.construct"/>
            params, req, parser, atc, answerTypeMap);
  } else {
    //just do a regular query if OWL is turned off
    qParser = req.getCore().getQueryPlugin("edismax")
            .createParser(qStr, localParams, params, req);
  }
  return qParser;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
/**
 * Train the answer model
 * <p>
 * Hint:
 * <pre>
 *  mvn exec:java -Dexec.mainClass=com.tamingtext.qa.AnswerTypeClassifier \
 *    -Dexec.args="dist/data/questions-train.txt en-answer.bin" \
 *    -Dmodel.dir=../../opennlp-models \
 *    -Dwordnet.dir=../../Wordnet-3.0/dict
 *  </pre>
 *
 * @param args
 * @throws IOException
 */
public static void main(String[] args) throws IOException {
    if (args.length < 2) {
        System.err.println("Usage: AnswerTypeClassifier <trainFile> <modelFile>");
        System.exit(1);
    }

    String trainFile = args[0];
    File outFile = new File(args[1]);
    String modelsDirProp = System.getProperty("model.dir");
    File modelsDir = new File(modelsDirProp);
    String wordnetDir = System.getProperty("wordnet.dir");

    InputStream chunkerStream = new FileInputStream(
            new File(modelsDir, "en-chunker.bin"));
    ChunkerModel chunkerModel = new ChunkerModel(chunkerStream);
    ChunkerME chunker = new ChunkerME(chunkerModel);
    InputStream posStream = new FileInputStream(
            new File(modelsDir, "en-pos-maxent.bin"));
    POSModel posModel = new POSModel(posStream);
    POSTaggerME tagger = new POSTaggerME(posModel);
    Parser parser = new ChunkParser(chunker, tagger);
    AnswerTypeContextGenerator actg = new AnswerTypeContextGenerator(new File(wordnetDir));
    //<start id="atc.train"/>
    AnswerTypeEventStream es = new AnswerTypeEventStream(trainFile,
            actg, parser);
    GISModel model = GIS.trainModel(100, new TwoPassDataIndexer(es, 3));//<co id="atc.train.do"/>
    GISModelWriter writer = new SuffixSensitiveGISModelWriter(model, outFile);
    writer.persist();
    //new DoccatModel("en", model).serialize(new FileOutputStream(outFile));
/*
<calloutlist>
    <callout arearefs="atc.train.do"><para>Using the event stream, which feeds us training examples, do the actual training using OpenNLP's Maxent classifier.</para></callout>
</calloutlist>
*/
    //<end id="atc.train"/>
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public WiseOwlQParser(String qstr, SolrParams localParams, SolrParams params, SolrQueryRequest req,
                       Parser parser, AnswerTypeClassifier atc,
                       Map<String,String> answerTypeMap) {
  super(qstr, localParams, params, req);
  this.parser = parser;
  this.atc = atc;
  this.atm = answerTypeMap;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public static void main(String args[]) throws IOException
{
	String wordnetDir = System.getProperty("wordnet.dir");
	//wordnetDir="WordNet-3.0/dict/";
	String question="Who is Abraham Lincoln?";
	AnswerTypeContextGenerator atcg=new AnswerTypeContextGenerator(new File(wordnetDir));
	String q=null;
    String modelsDirProp = System.getProperty("model.dir");
   // modelsDirProp="opennlp-models/";
    File modelsDir = new File(modelsDirProp);
    InputStream chunkerStream = new FileInputStream(
        new File(modelsDir,"en-chunker.bin"));
    ChunkerModel chunkerModel = new ChunkerModel(chunkerStream);
    ChunkerME chunker = new ChunkerME(chunkerModel);
    InputStream posStream = new FileInputStream(
        new File(modelsDir,"en-pos-maxent.bin"));
    POSModel posModel = new POSModel(posStream);
    POSTaggerME tagger =  new POSTaggerME(posModel);
    Parser parser = new ChunkParser(chunker, tagger);
    
    Parse query = ParserTool.parseLine(question,parser,1)[0];
	String[] context=atcg.getContext(query);
	for(int i=0;i<context.length;i++)
	{
		if(context[i].startsWith("hw=") || context[i].startsWith("mw="))
		{
			System.out.println(context[i].substring(3));
		}
	}
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public String[] getFocusNoun(String question) throws IOException
{
	String wordnetDir = System.getProperty("wordnet.dir");
	wordnetDir="WordNet-3.0/dict/";
	AnswerTypeContextGenerator atcg=new AnswerTypeContextGenerator(new File(wordnetDir));
	String q=null;
    String modelsDirProp = System.getProperty("model.dir");
    modelsDirProp="opennlp-models/";
    File modelsDir = new File(modelsDirProp);
    InputStream chunkerStream = new FileInputStream(
        new File(modelsDir,"en-chunker.bin"));
    ChunkerModel chunkerModel = new ChunkerModel(chunkerStream);
    ChunkerME chunker = new ChunkerME(chunkerModel);
    InputStream posStream = new FileInputStream(
        new File(modelsDir,"en-pos-maxent.bin"));
    POSModel posModel = new POSModel(posStream);
    POSTaggerME tagger =  new POSTaggerME(posModel);
    Parser parser = new ChunkParser(chunker, tagger);
    
    Parse query = ParserTool.parseLine(question,parser,1)[0];
	String[] context=atcg.getContext(query);
	String[] focus=new String[2];
	int p=0;
	for(int i=0;i<context.length;i++)
	{
		if(context[i].startsWith("hw=") || context[i].startsWith("mw="))
		{
			//System.out.println(context[i].substring(3));
			focus[p++]=context[i].substring(3);
		}
	}
	return focus;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public static Parse parse(String input) {
    ParserModel model = (ParserModel) models.get(ParserModel.class);
    Parser parser = ParserFactory.create(model);
    Parse topParses[] = ParserTool.parseLine(input, parser, tokenizer(), 1);
    return topParses[0];

}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public Parse[] parsePassageText(String p) throws InvalidFormatException{
	if (!modelsAreInitialized)init();
	//initialize 	 
	SentenceDetectorME sentenceDetector = new SentenceDetectorME(this.sentenceModel);
	NameFinderME nameFinder = new NameFinderME(this.nerModel);
	Parser parser = ParserFactory.create(
			this.parserModel,
			20, // beam size
			0.95); // advance percentage
	//find sentences, tokenize each, parse each, return top parse for each 	 	 
	String[] sentences = sentenceDetector.sentDetect(p);
	Parse[] results = new Parse[sentences.length];
	for (int i=0;i<sentences.length;i++){
		//String[] tks = SimpleTokenizer.INSTANCE.tokenize(sentences[i]);
		
		//StringTokenizer st = new StringTokenizer(tks[i]); 
		//There are several tokenizers available. SimpleTokenizer works best
		Tokenizer tokenizer = SimpleTokenizer.INSTANCE;
		for (int si = 0; si < sentences.length; si++) {
	        Span[] tokenSpans = tokenizer.tokenizePos(sentences[si]);
	        String[] tokens = Span.spansToStrings(tokenSpans, sentences[si]);
	        Span[] names = nameFinder.find(tokens);
	        for (int ni = 0; ni < names.length; ni++) {
	            Span startSpan = tokenSpans[names[ni].getStart()];
	            int nameStart = startSpan.getStart();
	            Span endSpan = tokenSpans[names[ni].getEnd() - 1];
	            int nameEnd = endSpan.getEnd();
	            String name = sentences[si].substring(nameStart, nameEnd);
	            System.out.println(name);
	        }
	    }
		String sent= StringUtils.join(tokenizer," ");
		System.out.println("Found sentence " + sent);
		Parse[] sentResults = ParserTool.parseLine(sent,parser, 1);
		results[i]=sentResults[0];
	}
	return results;
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
public AnswerTypeEventStream(String fileName, AnswerTypeContextGenerator atcg, Parser parser) throws IOException {
    this(fileName, null, atcg, parser);
}
 

import opennlp.tools.parser.Parser; //导入依赖的package包/类
@Override
public double scorePassage(Phrase q, Answer a, Passage p) {
	
	int countOfQuestionNPsInPassage = 0;
	try {
		//prep NLP tools
		if (!this.modelsAreInitialized) init();
		Parser parser = ParserFactory.create(this.parserModel, 20, 0.95);

		//create question parse
		Parse[] questionParse = ParserTool.parseLine(q.text, parser, 1);

		//create passage parses (one for each sentence)
		String[] passageSentences = this.DivideIntoSentences(p);
		Parse[] passageParses = new Parse[passageSentences.length];
		Parse[] tempParse;
		for (int i=0; i < passageSentences.length; i++) {
			tempParse = ParserTool.parseLine(passageSentences[i], parser, 1);
			passageParses[i] = tempParse[0];
		}
		
		//retrieve NPs from the question parse
		navigateTree(questionParse, 0, questionNPs);

		//retrieve NPs from the passage parse
		for (int i=0; i < passageParses.length; i++) {
			navigateTree(passageParses, i, passageNPs);				
		}
		
		//count the number of question NPs that are in the passage NP set (A)
		for (String qNP: questionNPs) {
			for (String pNP: passageNPs) {
				//System.out.println("comparing " + qNP + " with " + pNP);
				if (qNP.equals(pNP)) {
					//System.out.println("match found");
					countOfQuestionNPsInPassage++;
				}
			}
		}
		//System.out.println(countOfQuestionNPsInPassage);
		
		//count the number of all NPs that are in the passage NP set (B)
		//passageNPs.size();
		
	} catch (InvalidFormatException e) {
		e.printStackTrace();
	}

	//calculate A/B and return as the score
	//System.out.print("******** score:  " + (double)countOfQuestionNPsInPassage/passageNPs.size() + "  *******");
	//System.out.println(" count:  " + passageNPs.size() + "  *******");
	if (passageNPs.size() == 0)
		return 0;
	else
		return (double)countOfQuestionNPsInPassage/passageNPs.size();
}