Examples of opennlp.tools.postag.POSTaggerME

opennlp.tools.postag.POSTaggerME
A part-of-speech tagger that uses maximum entropy. Tries to predict whether words are nouns, verbs, or any of 70 other POS tags depending on their surrounding context.

    /**
     * @param language
     * @return
     */
    private POSTaggerME initTagger(String language) {
        POSTaggerME posTagger;
        try {
            POSModel posModel = openNLP.getPartOfSpeachModel(language);
            if(posModel != null){
                posTagger = new POSTaggerME(posModel);
            } else {
                log.debug("No POS Model for language {}",language);
                posTagger = null;
            }
        } catch (IOException e) {

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        }
        if(posTagger == null && !posTaggerNotAvailable){
            try {
                POSModel posModel = openNLP.getPartOfSpeachModel(language);
                if(posModel != null){
                    posTagger = new POSTaggerME(posModel);
                } else {
                    log.debug("No POS Model for language '{}'",language);
                    posTaggerNotAvailable = true;
                }
            } catch (IOException e) {

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                throw new IllegalArgumentException(
                    "The parsed offset MUST NOT be a negative number (offset="+offset+")");
            }
            this.offset = offset;
            Span[] tokenSpans = getTokenizer().tokenizePos(sentence);
            POSTaggerME tagger = getPosTagger();
            ChunkerME chunker = getChunker();
            PosTypeChunker posTypeChunker = getPosTypeChunker();
            String[] tokens = new String[tokenSpans.length];
            for(int ti = 0; ti<tokenSpans.length;ti++) {
                tokens[ti] = tokenSpans[ti].getCoveredText(sentence).toString();
            }
            String[][] posTags;
            double[][] posProbs;
            Span[] chunkSpans;
            double[] chunkProps;
            if(tagger != null){
                posTags = new String[tokens.length][];
                posProbs = new double[tokens.length][];
                //get the topK POS tags and props and copy it over to the 2dim Arrays
                Sequence[] posSequences = tagger.topKSequences(tokens);
                //extract the POS tags and props for the current token from the
                //posSequences.
                //NOTE: Sequence includes always POS tags for all Tokens. If
                //      less then posSequences.length are available it adds the
                //      best match for all followings.

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          //File f = new File(d, "tag.bin.gz");
          MaxentModel posModel = new opennlp.maxent.io.SuffixSensitiveGISModelReader(f).getModel();
          if (useTagDictionary) {
            File td = new File(d, "tagdict");
            TagDictionary tagDictionary = new POSDictionary(td.getAbsolutePath()); //null;
            posTagger = new POSTaggerME((AbstractModel) posModel, tagDictionary);
          } else {
            // f = new File(d, "dict.bin.gz");
            Dictionary dictionary = null; // new Dictionary();
            posTagger = new POSTaggerME((AbstractModel) posModel, dictionary);


          }
        }

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  private int[] attachments;


  public Parser(ParserModel model, int beamSize, double advancePercentage) {
    this(model.getBuildModel(), model.getAttachModel(), model.getCheckModel(), 
        new POSTaggerME(model.getParserTaggerModel()), 
        new ChunkerME(model.getParserChunkerModel(),
        ChunkerME.DEFAULT_BEAM_SIZE,
        new ParserChunkerSequenceValidator(model.getParserChunkerModel()),
        new ChunkContextGenerator(ChunkerME.DEFAULT_BEAM_SIZE)),
        model.getHeadRules(),

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            if(model != null) {
                log.debug("POS Tagger Model {} for lanugage '{}' version: {}",
                    new Object[]{model.getClass().getSimpleName(), 
                                 model.getLanguage(), 
                                 model.getVersion() != null ? model.getVersion() : "undefined"});
                return new POSTaggerME(model);
            }
        } catch (Exception e) {
            log.warn("Unable to load POS model for language '"+language+"'!",e);
        }
        log.debug("POS tagging Model for Language '{}' not available.", language);

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