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The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.

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package edu.ucla.sspace.tools;

import edu.ucla.sspace.basis.BasisMapping;

import edu.ucla.sspace.matrix.Matrix;
import edu.ucla.sspace.matrix.MatrixIO;
import edu.ucla.sspace.matrix.MatrixIO.Format;

import edu.ucla.sspace.util.BoundedSortedMultiMap;
import edu.ucla.sspace.util.MultiMap;
import edu.ucla.sspace.util.SerializableUtil;

import java.io.File;

import java.util.ArrayList;
import java.util.List;


/**
 * @author Keith Stevens
 */
public class SelectTopKWords {
    public static void main(String[] args) throws Exception {
        // Load the basis mapping.
        BasisMapping basis = 
            SerializableUtil.load(new File(args[0]));

        // Create the top 10 lists for each topic in the word space.
        List> topTerms = new ArrayList>();
        Matrix m = MatrixIO.readMatrix(new File(args[1]), Format.DENSE_TEXT);
        for (int c = 0; c < m.columns(); ++c)
            topTerms.add(new BoundedSortedMultiMap(10));

        for (int r = 0; r < m.rows(); ++r) {
            String term = basis.getDimensionDescription(r);
            for (int c = 0; c < m.columns(); ++c)
                topTerms.get(c).put(m.get(r, c), term);
        }

        for (MultiMap topicTerms : topTerms) {
            for (String term : topicTerms.values())
                System.out.printf("%s ", term);
            System.out.println();
        }
    }
}