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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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/*
 * Copyright 2010 Keith Stevens
 *
 * This file is part of the S-Space package and is covered under the terms and
 * conditions therein.
 *
 * The S-Space package is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as published
 * by the Free Software Foundation and distributed hereunder to you.
 *
 * THIS SOFTWARE IS PROVIDED "AS IS" AND NO REPRESENTATIONS OR WARRANTIES,
 * EXPRESS OR IMPLIED ARE MADE.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, WE MAKE
 * NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY
 * PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE OR DOCUMENTATION
 * WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER
 * RIGHTS.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program. If not, see .
 */

package edu.ucla.sspace.matrix;

import edu.ucla.sspace.common.statistics.LogLikelihoodTest;


/**
 * Transforms a matrix using the log-likelihood weight.  The input matrix is
 * assumed to have non-negative values and be formatted as rows representing
 * terms and columns representing terms.  Each matrix cell indicates the number
 * of times the row's word occurs within the some range of the column's word.
 * Although the log likelihood typically requires much more than this, an
 * estimation is used that utilizes only the occurrence frequency counts based.
 * See the following papers for details and analysis:
 *
 *  Pado, S. and Lapata, M.
 * (2007) Dependnecy-Based Construction of Semantic Space Models.
 * Association of Computational Linguistics, 33.
 
 * @author Keith Stevens
 */
public class LogLikelihoodTransform extends SignificanceMatrixTransform {

    public LogLikelihoodTransform() {
        super(new LogLikelihoodTest());
    }
}