Example usage for opennlp.tools.chunker ChunkerME ChunkerME

Introduction

In this page you can find the example usage for opennlp.tools.chunker ChunkerME ChunkerME.

Prototype

public ChunkerME(ChunkerModel model) 

Source Link

Document

Initializes the current instance with the specified model.

Usage

From source file:hrpod.tools.nlp.NLPTools.java

public String[] tokenize(String text) {

    String[] chunkStrings = null;

    try {//from ww  w.  jav a  2  s  . c om
        TokenizerME wordBreaker = new TokenizerME(getTokenModel());
        POSTaggerME posme = new POSTaggerME(getPosModel());
        ChunkerME chunkerME = new ChunkerME(getChunkerModel());

        //words is the tokenized sentence
        String[] words = wordBreaker.tokenize(text);
        //posTags are the parts of speech of every word in the sentence (The chunker needs this info)
        String[] posTags = posme.tag(words);
        //chunks are the start end "spans" indices to the chunks in the words array
        Span[] chunks = chunkerME.chunkAsSpans(words, posTags);
        //chunkStrings are the actual chunks
        chunkStrings = Span.spansToStrings(chunks, words);
        //for (int i = 0; i < chunks.length; i++) {
        //    if (chunks[i].getType().equals("NP")) {
        //        System.out.println("NP: \n\t" + chunkStrings[i]);

        //String[] split = chunkStrings[i].split(" ");
        //List<String> ngrams = ngram(Arrays.asList(split), N, " ");
        //System.out.println("ngrams:");
        //for (String gram : ngrams) {
        //  System.out.println("\t" + gram);
        //}
        //}
        //}
    } catch (Exception e) {
        logger.error("Error in tokenize", e);
    }

    return chunkStrings;

}

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From source file:org.sglover.nlp.CoreNLPEntityTagger.java

@Override
protected Entities getEntitiesImpl(String content) {
    Entities namedEntities = Entities.empty();

    SentenceModel sentenceModel = sentenceModels.get("en");
    SentenceDetector sentenceDetector = new SentenceDetectorME(sentenceModel);
    String[] sentences = sentenceDetector.sentDetect(content);

    TokenizerModel tm = tokenizerModels.get("en");
    TokenizerME wordBreaker = new TokenizerME(tm);

    for (String sentence : sentences) {
        String[] tokens = wordBreaker.tokenize(sentence);

        List<TextAnnotation> allTextAnnotations = new LinkedList<TextAnnotation>();

        POSModel posModel = posModels.get("en");
        POSTaggerME posme = new POSTaggerME(posModel);
        String[] posTags = posme.tag(tokens);

        List<String> npTokens = new LinkedList<>();

        ChunkerModel chunkerModel = chunkerModels.get("en");
        ChunkerME chunkerME = new ChunkerME(chunkerModel);
        Span[] chunks = chunkerME.chunkAsSpans(tokens, posTags);
        String[] chunkStrings = Span.spansToStrings(chunks, tokens);
        for (int i = 0; i < chunks.length; i++) {
            String chunkString = chunkStrings[i];
            logger.info("Chunk = " + chunkString + ", type = " + chunks[i].getType());
            if (chunks[i].getType().equals("NP")) {
                npTokens.add(chunkString);
            }//from   w ww .jav  a2  s .  com
        }

        // findEntities(namedEntities, allTextAnnotations,
        // npTokens.toArray(new String[0]));
        findEntities(namedEntities, allTextAnnotations, tokens);
    }

    return namedEntities;
}

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From source file:org.apache.stanbol.commons.opennlp.OpenNLP.java

/**
 * Getter for the {@link Chunker} for a given language
 * @param language the language//from w  ww  . ja v a 2 s  .c o  m
 * @return the {@link Chunker} or <code>null</code> if no model is present
 * @throws InvalidFormatException in case the found model data are in the wrong format
 * @throws IOException on any error while reading the model data
 */
public Chunker getChunker(String language) throws IOException {
    ChunkerModel chunkerModel = getChunkerModel(language);
    if (chunkerModel != null) {
        return new ChunkerME(chunkerModel);
    } else {
        log.debug("No Chunker Model for language {}", language);
        return null;
    }
}

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From source file:org.dbpedia.spotlight.spot.OpenNLPNGramSpotter.java

/**Extracts noun-phrase n-grams from the given piece of input text. 
 * @param text  A Text object containing the input from where to extract NP n-grams
 * @return A list of SurfaceFormOccurrence objects.
 *///w w w  . ja v a  2 s  .c  o  m
protected List<SurfaceFormOccurrence> extractNPNGrams(Text text) {
    String intext = text.text();
    //System.out.println("\n\nRR- nextractNPNGrams(...) method called! with text: " + intext + "\n\n");
    List<SurfaceFormOccurrence> npNgramSFLst = new ArrayList<SurfaceFormOccurrence>();
    SentenceDetectorME sentenceDetector = new SentenceDetectorME((SentenceModel) sentenceModel);
    TokenizerME tokenizer = new TokenizerME((TokenizerModel) tokenModel);
    POSTaggerME posTagger = new POSTaggerME((POSModel) posModel);
    ChunkerME chunker = new ChunkerME((ChunkerModel) chunkModel);

    Span[] sentSpans = sentenceDetector.sentPosDetect(intext);
    for (Span sentSpan : sentSpans) {
        String sentence = sentSpan.getCoveredText(intext).toString();
        int start = sentSpan.getStart();
        Span[] tokSpans = tokenizer.tokenizePos(sentence);
        String[] tokens = new String[tokSpans.length];
        // System.out.println("\n\nTokens:");
        for (int i = 0; i < tokens.length; i++) {
            tokens[i] = tokSpans[i].getCoveredText(sentence).toString();
            // System.out.println(tokens[i]);
        }
        String[] tags = posTagger.tag(tokens);
        Span[] chunks = chunker.chunkAsSpans(tokens, tags);
        for (Span chunk : chunks) {
            if ("NP".equals(chunk.getType())) {
                //Note: getStart()/getEnd() methods of Chunk spans only give the start and end token indexes of the chunk.
                //The actual Start/End positions of the chunk in the sentence need to be extracted from POS sentenceSpans.
                //They are offsets from the begining of the sentence in question. Need to add the start postion of the sentence
                //to compute the actual start/end offsets from the begining of the input text.
                int begin = tokSpans[chunk.getStart()].getStart();
                int end = tokSpans[chunk.getEnd() - 1].getEnd();
                List<Map<String, Integer>> ngrampos = extractNGramPos(chunk.getStart(), chunk.getEnd() + -1);
                extractNGrams(ngrampos, start, text, tokSpans, npNgramSFLst);
            }
        }
    }
    return npNgramSFLst;
}

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