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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.wordsi;

import edu.ucla.sspace.dependency.DependencyTreeNode;

import edu.ucla.sspace.vector.SparseDoubleVector;


/**
 * An interface for generating context vectors from raw unparsed text.  A
 * context vector can be generated from a sliding window of text.  {@link
 * ContextGenerator}s are a subcomponent of {@link ContextExtractor}s.  As a
 * {@link ContextExtractor} examines text and finds a word which is being
 * represented in the {@link Wordsi} space, it will call the {@link
 * ContextGenerator} and pass the created context vector to a {@link Wordsi}
 * implementation.
 *
 * 
 *
 * {@link ContextGenerator}s are recomended to be made serializable.  They will
 * serve as the core representation method of {@link Wordsi} implementations and
 * can thus be re-used in multiple evaluations.  For example, after training a
 * {@link Wordsi} model, it may need to be evaluated in a psuedo-word
 * disambiguation task or a SemEval task.  In both cases, the feature space must
 * remain the same turing training and evaluation.
 *
 * 
 *
 * For evaluation purposes, an added option is available: a read only mode.
 * When in read only mode, {@link ContextGenerator}s should not create any new
 * features.  If some co-occurring term does not exist in the feature space, it
 * should be left out of the context vector, only feature which already exist in
 * the space should contribute to the context vector.  In standard mode, the
 * generator is permitted to decided which words should serve as features using
 * any method.
 *
 * @author Keith Stevens
 */
public interface DependencyContextGenerator {

    /**
     * Returns a {@link SparseDoubleVector} that represents the context composed
     * of the set of {@code prevWords} before the focus word and the set of
     * {@code nextWords} after the focus word.  Since sparse vectors are
     * returned, if a second order vector is generated, it is recommended that
     * the vector also be sparsed or have very few dimensions.
     */
    SparseDoubleVector generateContext(DependencyTreeNode[] tree,
                                        int focusIndex);

    /**
     * Returns the maximum number of dimensions used to represent any given
     * context.
     */
    int getVectorLength();

    /**
     * Sets the read only mode of the {@link ContextGenerator}.  When set to
     * read only, it prevents any new features from being generated.
     */
    void setReadOnly(boolean readOnly);
}