package edu.northwestern.at.utils.corpuslinguistics.postagger.smoothing.lexical;

/*	Please see the license information at the end of this file. */

import java.util.*;

import edu.northwestern.at.utils.corpuslinguistics.lexicon.*;
import edu.northwestern.at.utils.corpuslinguistics.postagger.*;
import edu.northwestern.at.utils.math.*;

/**	Additive lexical smoother.
 *
 *	<p>
 *	A lexical smoother which adds a small positive value to each
 *	(word, tag) count to avoid zero probabilities.  When the value
 *	added is 1, this is Laplace smoothing.  When the value added is
 *	0.5, this is Lidstone smoothing.
 *	</p>
 *
 *	<p>
 *	This implementation uses 0.5 as the adjustment value (Lidstone
 *	smoothing).
 *	</p>
 */

public class AdditiveLexicalSmoother
	extends AbstractLexicalSmoother
	implements LexicalSmoother
{
	/**	True if debugging enabled. */

	protected boolean debug	= true;

	/**	Additive adjustment value	= 0.5 (Lidstone smoothing).
	 */

	protected double additiveAdjustmentValue	= 0.5D;

	/**	Create an additive lexical smoother.
	 */

	public AdditiveLexicalSmoother()
	{
		super();
	}

	/**	Get lexically smoothed probability of a word given a tag.
	 *
	 *	@param	word	The word.
	 *	@param	tag		The part of speech tag.
	 *
	 *	@return			Lexically smoothed probability of word given tag,
	 *					e.g., p( word | tag ).
	 */

	public Probability lexicalProbability( String word , String tag )
	{
								//	See if the lexical probability
								//	p( word | tag ) is in the cache.

		Probability result	=	null;

		if ( cachedLexicalProbabilities != null )
		{
			result	= cachedLexicalProbabilities.get( word , tag );
		}
								//	If the probability isn't in the
								//	cache, compute it.

		if ( result == null )
		{
								//	Total number of times this
								//	tag appeared in the training data.

			Lexicon lexicon	= partOfSpeechTagger.getLexicon( word );

			int tagCount	=
				Math.max( lexicon.getCategoryCount( tag ) , 1 );

								//	Total number of times this word
								//	appeared with this tag in the
								//	training data.

			int catCount	=
				partOfSpeechTagger.getTagCount( word , tag );

								//	Set of possible part of speech
								//	tags for this word.

			List<String> tags	=
				partOfSpeechTagger.getTagsForWord( word );

								//	Calculate smoothed probability
								//	using additive smoothing.

			double prob		=
				( catCount + additiveAdjustmentValue ) /
					( (double)tagCount +
						( additiveAdjustmentValue  * tags.size() ) );

								//	Store computed probability in
								//	the cache.
			try
			{
				result	= new Probability( prob );
			}
			catch ( Exception e )
			{
				if ( debug )
				{
					logger.logError
					(
						"word=" + word + ", tag=" + tag +
						", catCount=" + catCount +
						", adj=" + additiveAdjustmentValue +
						", tagCount=" + tagCount +
							", tags.size=" + tags.size() +
						", prob=" + prob
					);
				}

				result	= new Probability( 1.0 );
			}

			if ( cachedLexicalProbabilities != null )
			{
				cachedLexicalProbabilities.put( word , tag , result );
			}
		}

		return result;
	}
}

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   Northwestern University
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files (the "Software"), to deal with the Software without
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Software is furnished to do so, subject to the following
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    * Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimers.

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      Northwestern University, nor the names of its contributors may be
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