Package org.carrot2.text.preprocessing

Examples of org.carrot2.text.preprocessing.PreprocessingContext$AllWords

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       */
    case ARABIC:
      // Intentional fall-through.

    default:
      return new ExtendedWhitespaceTokenizer();
    }
  }
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    static ITokenizer createTokenizer() {
      try {
        return new ChineseTokenizer();
      } catch (Throwable e) {
        return new ExtendedWhitespaceTokenizer();
      }
    }
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    }
    return solrStopWords.get(fieldName);
  }

  public ILexicalData getLexicalData(LanguageCode languageCode) {
    final ILexicalData carrot2LexicalData = carrot2LexicalDataFactory
        .getLexicalData(languageCode);

    return new ILexicalData() {
      public boolean isStopLabel(CharSequence word) {
        // Nothing in Solr maps to the concept of a stop label,
        // so return Carrot2's default here.
        return carrot2LexicalData.isStopLabel(word);
      }

      public boolean isCommonWord(MutableCharArray word) {
        // Loop over the fields involved in clustering first
        for (String fieldName : fieldNames) {
          for (CharArraySet stopWords : getSolrStopWordsForField(fieldName)) {
            if (stopWords.contains(word)) {
              return true;
            }
          }
        }
        // Check default Carrot2 stop words too
        return carrot2LexicalData.isCommonWord(word);
      }
    };
  }
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      return;
    }

    // Test with Maltese so that the English clustering performed in other tests
    // is not affected by the test stopwords and stoplabels.
    ILexicalData lexicalData = preprocessing.lexicalDataFactory
        .getLexicalData(LanguageCode.MALTESE);

    for (String word : wordsToCheck.split(",")) {
      if (!lexicalData.isCommonWord(new MutableCharArray(word))
          && !lexicalData.isStopLabel(word)) {
        clusters.add(new Cluster(word));
      }
    }
  }
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     * one <code>language</code>.
     */
    private void cluster(LanguageCode language)
    {
        // Preprocessing of documents
        final PreprocessingContext context = preprocessingPipeline.preprocess(documents,
            query, language);

        // Further processing only if there are words to process
        clusters = Lists.newArrayList();
        if (context.hasLabels())
        {
            // Term-document matrix building and reduction
            final VectorSpaceModelContext vsmContext = new VectorSpaceModelContext(
                context);
            final ReducedVectorSpaceModelContext reducedVsmContext = new ReducedVectorSpaceModelContext(
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public class SimpleLabelAssigner implements ILabelAssigner
{
    public void assignLabels(LingoProcessingContext context, DoubleMatrix2D stemCos,
        IntIntOpenHashMap filteredRowToStemIndex, DoubleMatrix2D phraseCos)
    {
        final PreprocessingContext preprocessingContext = context.preprocessingContext;
        final int firstPhraseIndex = preprocessingContext.allLabels.firstPhraseIndex;
        final int [] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
        final int [] mostFrequentOriginalWordIndex = preprocessingContext.allStems.mostFrequentOriginalWordIndex;
        final int desiredClusterCount = stemCos.columns();
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public class UniqueLabelAssigner implements ILabelAssigner
{
    public void assignLabels(LingoProcessingContext context, DoubleMatrix2D stemCos,
        IntIntOpenHashMap filteredRowToStemIndex, DoubleMatrix2D phraseCos)
    {
        final PreprocessingContext preprocessingContext = context.preprocessingContext;
        final int firstPhraseIndex = preprocessingContext.allLabels.firstPhraseIndex;
        final int [] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
        final int [] mostFrequentOriginalWordIndex = preprocessingContext.allStems.mostFrequentOriginalWordIndex;
        final int desiredClusterCount = stemCos.columns();
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    /**
     * Discovers labels for clusters.
     */
    void buildLabels(LingoProcessingContext context, ITermWeighting termWeighting)
    {
        final PreprocessingContext preprocessingContext = context.preprocessingContext;
        final VectorSpaceModelContext vsmContext = context.vsmContext;
        final DoubleMatrix2D reducedTdMatrix = context.reducedVsmContext.baseMatrix;
        final int [] wordsStemIndex = preprocessingContext.allWords.stemIndex;
        final int [] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
        final int [] mostFrequentOriginalWordIndex = preprocessingContext.allStems.mostFrequentOriginalWordIndex;
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    @Override
    public PreprocessingContext preprocess(List<Document> documents, String query,
        LanguageCode language)
    {
        final PreprocessingContext context = new PreprocessingContext(
            LanguageModel.create(language, stemmerFactory, tokenizerFactory,
                lexicalDataFactory), documents, query);

        tokenizer.tokenize(context);
        caseNormalizer.normalize(context);
        languageModelStemmer.stem(context);
        stopListMarker.mark(context);
        phraseExtractor.extractPhrases(context);
        labelFilterProcessor.process(context);
        documentAssigner.assign(context);

        context.preprocessingFinished();
        return context;

    }
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     */
    @Override
    public PreprocessingContext preprocess(List<Document> documents, String query,
        LanguageCode language)
    {
        final PreprocessingContext context = new PreprocessingContext(
            LanguageModel.create(language, stemmerFactory, tokenizerFactory,
                lexicalDataFactory), documents, query);

        tokenizer.tokenize(context);
        caseNormalizer.normalize(context);
        languageModelStemmer.stem(context);
        stopListMarker.mark(context);

        context.preprocessingFinished();
        return context;
    }
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