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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.common.SemanticSpace;

import java.util.BitSet;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;

import java.io.BufferedReader;


/**
 * This base class accepts or rejects key words based on a set of {@code
 * acceptedWords} and dispatches calls to a {@link ContextExtractor} so that
 * {@link Wordsi} sub-classes will be called with each generated vector.
 *
 * @author Keith Stevens
 */
public abstract class BaseWordsi implements Wordsi, SemanticSpace {

    /**
     * The set of words which should be represented by {@link Wordsi}.
     */
    private final Set acceptedWords;

    /**
     * The {@link ContextExtractor} responsible for parsing documents and
     * creating context vectors.
     */
    private ContextExtractor extractor;

    /**
     * Creates a new {@link BaseWordsi}.
     *
     * @param acceptedWords The set of words which {@link Wordsi} should
     *        represent, may be {@code null} or empty.
     * @param trackSecondaryKeys If true, secondary key assignments will be
     *        tracked
     */
    public BaseWordsi(Set acceptedWords,
                      ContextExtractor extractor) {
        this.acceptedWords = acceptedWords;
        this.extractor = extractor;
    }

    /**
     * {@inheritDoc}
     */
    public boolean acceptWord(String word) {
        return acceptedWords == null || 
               acceptedWords.isEmpty() ||
               acceptedWords.contains(word);
    }

    /**
     * {@inheritDoc}
     */
    public String getSpaceName() {
        return "Wordsi";
    }

    /**
     * {@inheritDoc}
     */
    public int getVectorLength() {
        return extractor.getVectorLength();
    }

    /**
     * {@inheritDoc}
     */
    public void processDocument(BufferedReader document) {
        extractor.processDocument(document, this);
    }
}