Example usage for org.deeplearning4j.text.tokenization.tokenizer Tokenizer hasMoreTokens

Introduction

In this page you can find the example usage for org.deeplearning4j.text.tokenization.tokenizer Tokenizer hasMoreTokens.

Prototype

boolean hasMoreTokens();

Source Link

Document

An iterator for tracking whether more tokens are left in the iterator not

Usage

From source file:org.knime.ext.textprocessing.dl4j.nodes.embeddings.apply.WordVectorApplyNodeModel.java

License:Open Source License

/**
 * Replaces each word contained in a document with its corresponding word vector. If a word from the document is not
 * contained in the used {@link WordVectors} model it will be skipped. The output is a {@link ListCell} containing
 * {@link ListCell}s containing the word vectors as {@link DoubleCell}s.
 *
 * @param wordVec the {@link WordVectors} model to use
 * @param document the document to use//from  w  ww  .j  a  v a  2  s. c om
 * @return {@link ListCell} of {@link ListCell}c of {@link DoubleCell}s containing converted words
 */
private ListCell replaceWordsByWordVector(final WordVectors wordVec, final String document) {
    final TokenizerFactory tokenizerFac = new DefaultTokenizerFactory();
    tokenizerFac.setTokenPreProcessor(new CommonPreprocessor());

    final Tokenizer t = tokenizerFac.create(document);
    final List<ListCell> listCells = new ArrayList<ListCell>();

    while (t.hasMoreTokens()) {
        final String word = t.nextToken();
        if (!word.isEmpty()) {
            if (wordVec.hasWord(word)) {
                listCells.add(wordToListCell(wordVec, word));
            } else {
                m_unknownWords.add(word);
            }
        }
    }
    return CollectionCellFactory.createListCell(listCells);
}

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