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From "Andrew Schetinin" <>
Subject Search for synonyms - implemenetation for review
Date Mon, 06 Mar 2006 14:20:00 GMT
Dear all,
Me and my college, Mr. Ziv Gome, would like to present here an
implementation of synonyms search that we use in our server.
Probably it will be interesting for those who worked on synonyms, or
going to implement synonyms search.
We hope that this mail will raise interesting ideas and will result in
useful results :-)

And I would like to mention that it was Ziv who inspired this research
and development.
He did most of the analytical work and basically invented this entire
1. Background

There is an interesting discussion about synonyms in Lucene mailing list
which suggests expanding the search query with additional clauses for
synonyms penalized using boost factor.
If we understood correctly, this is employed in the "WordNet" project
for English synonyms. 
When we started working with synonyms, we implemented exactly that at
first, just without the boost (or, better to say, penalty) factor.
Mr. Erik Hatcher in his book refers to synonyms in a different way -
injecting them at indexing time - we did not quite liked that because it
does not allow searching without synonyms, and we wanted
enabling/disabling this feature from UI.. 

2. First synonyms search implementation and related problems

As I said, at the beginning we simply expanded the query with the
additional term queries for all synonyms. 
For example, looking for 
	car AND tire
Would result in 
	(car OR auto OR automobile OR motorcar) AND (tire OR tyre)
At first we did not even penalized the synonyms with the boost factor.
The results were very surprising :-)

As those who read about Lucene scoring implementation know, there is an
IDF factor which is used by Lucene for normalization of different terms
(inversed document frequency for a term).
The formula for this factor is 
	IDF = log( numDocs / (docFreq + 1) ) + 1
where numDocs is the total number of documents in the index, and docFreq
is the count of documents containing the given term.
As it is possible to see, the term not contained in the index at all
gets the highest IDF value, and the term contained in every single
document gets the lowest IDF value.
The basic idea (as far as I got it) is that a very common term does not
weight much (words like "program"), while a rare term may be very
meaningful (words like... say, "Lucene" :-) )
At the search time Lucene engine summarizes squares of IDF for all
terms, and normalizes all term queries back basing on the square root of
this sum (somewhat simplified explanation).
Basically, each term gets its weight like
	termWeigth = IDF * boost
	sumOfSquaredWeights = 1 / SQRT( SUM( termWeight[i] *
termWeight[i] ) ) 
	weight[i] = IDF * ( termWeigth  / sumOfSquaredWeights )
Another important thing that will be discussed here is term frequency in
the document (based on the count of term appearances in the document),
which is calculated in Lucene as:
	tf = sqrt( freq )
Refer to the book I mentioned earlier, or Lucene on-line documentation
for more information. 

Now, that means, that looking for a single words "tire" without synonyms
will normalize this word with normalization factor 1.0
Let's suppose that its synonym "tyre" does not appear in the index, and
we search for both -  (tire OR tyre)
Surprisingly, now the result scores is much-much lower - only because of
the normalization factor that divides the query weight between two
It is good for boolean operators (which we used here, so Lucene
mathematics is perfectly okay), but it does not goes well logically.

Unfortunately, a penalty factor would not help much, only somewhat lower
the effect.

3. Some ideas we had and the new, hopefully better, implementation 

We started thinking what to do about that (one could say that we would
need to think ahead before implementation :-) and he would be right).
First, we decided that a penalty of 0.8 should be applied to the
synonyms, as they are not exactly the word the user specified in the
search string.
But the penalty is not enough to correct the situation.

Then we thought that the synonym is basically another occurrence of the
searched word, and we thought that if only the query is normalized using
IDFs of the searched words and not synonyms, the situation would look
This would require two things: 

First, we need to take only the IDF of the original word for
normalization, like in the query:
	(car OR auto OR automobile OR motorcar) AND (tire OR tyre)
Lucene implementation would SUM squared weights of all words and
synonyms, while we need to take only two original words (car AND tire),
and normalize all the synonyms using normalization factors of the
original words.
The idea is that this would give to the synonyms the weight of the
original word, which is what seems to be more natural if we think of the
synonyms like another occurrences of the original word.
This causes the result document score to do not change when we search
for the word with/without synonyms, and the synonyms do not appear in
the document -exactly what we wanted.
This is not hard to do using a variation of DisjunctionQuery, originally
invented by Chuck Williams and found in the Lucene users' list (please
correct me if I'm wrong).

Second, we need to calculate term frequency as something like (say, for
the (tire OR tyre) case )
	tf = sqrt( freq[tire] + 0.8 * freq[tyre] )
The idea is that if the word "tire" appeared 10 appearances of the
synonym "tyre" in a document will be counted like 8 appearances of the
original word "tire", and it will be multiplied on the normalized weight
of the original word "tire".
Note that this 0.8 normalization cannot be applied as the regular query
boost in Lucene, because the original query boost is used in the query
normalization and it compensates itself in a case of a single word, and
we do not need such behavior.
The problem with that is that the tf() function is calculated for each
frequency at the scoring time, and while we may square it back in the
parent scorer to get the original frequence and sum it, it will happen
for each single document returned in the result, which is unnecessary
additional work. Because of that we simply use a custom Similarity class
which returns the unaffected frequency value from tf() function, and a
special SynonymScorer which accumulates the frequences, and then
calculates tf() of their sum.

Additionally, we get a new problem - if we apply IDF of the original
word to the synonym, it would not work good in cases when the original
word is rare and the synonym is frequent. The documents found because of
synonyms may get unreasonable high scores because of high term frequency
of the synonym and high weight of the rare original word. Therefore we
need a compensation factor like the following
	tf = sqrt( freq[tire] + 0.8 * freq[tyre] * idfFactor )
	idfFactor = (IDF[synonym] * IDF[synonym]) / (IDF[word] *
Note that we take relation of IDF squares here, From our experience,
relation of squares behave better here than simple relation of IDFs.

Now, this is something hard to do, as the term frequency calculation
occurs at the scoring time, long after the normalization, and cannot be
affected without changing TermQuery and TermScorer classes, and we did
not like the idea of touching them. What we did was playing with the
normalization factor at the time of query normalization in such a way
that the resulting synonym TermQuery's weight will incorporate the
necessary boosts which will be applied to tf() function result at the
time of scoring in exactly the same manner like if tf() function would
know about these factors.
Assuming we know that tf() function is a simple square root, we apply
the square root to both the synonym boost and the IDF squares relation.

Please take a look on the source code provided at the end of this mail.
It contains the necessary comments.
It was dififcult to extract a small subset of classes, because there are
many other dependancies and things unrelated to synonyms.
I cut a lot of code and change package naming (replaced all our packages
by for simplification) and only checked that it
compiles afterwards. 
Feel free to to reuse the sources, just don't blame us if something
brokes in result :-)
	SynonymsQuery and SynonymsWeight classes - the central point of
the algorithm
	SynonymScorer - the scorer which implements the trick with tf()
	SynonymSimilarity - also for the trick with tf() function
It may look somewhat complicated, but in fact it is quite simple. 

4. Conclusion

Synonyms search is difficult to do right, and there is hardly a perfect
implementation which would work correctly for all cases. 
There are some pitfalls which were not mentioned in this e-mail at all,
like synonyms which are context-specific, or synonyms with weak relation
to the original word.
In practice, it's hardly possible to handle all the cases properly,
partly because a regular synonyms dictionary is a simple string map, and
it does not try to categorize or weight the synonyms.

We think that we got a relatively good solution for the synonyms search.
We like the search results we get now after implementing this idea.

We're looking forward to hear your opinions and other ideas.
I will be happy to answer your questions, either in this mailing list or
at my other email aschetinin_gmail_com (replace underscores).
Thank you for your time and consideration,
Andrew Schetinin

Appendix A. Sources


 * Copyright (c) 2006 Entopia Ltd. All Rights Reserved.

import org.apache.lucene.index.IndexReader;

import java.util.ArrayList;

 * This query is purposed for working with synonyms of one word.
 * The general idea is:
 * 1. The query is normalized by the original word weight only, not
taking into account its synonyms.
 * 2. Weights of the synonyms are scaled (normalized) using normlization
factor of the original word,
 *    also applying some penalty (since synonyms incure uncertancy into
the search), and applying
 *    additional normalization that takes into account weight relation
between the original word
 *    and the synonym (an attempt to bring them to the same scale).
 * 3. When calculating the final score for full text search, we try to
interpret synonyms just like
 *    additional appearences of the original word, somewhat penalized
because synonyms incure
 *    uncertancy into searches. This is what the algorithm does, and
thus it requires a special
 *    scorer SynonymScorer and special similarity
 * Most important functions are: SynonymsWeight#sumOfSquaredWeights()
and SynonymsWeight#normalize().
 * Other important functions are: SynonymsWeight#scorer() and
 * @author <a href="mailto:aschetinin_entopia_com">Andrew S.</a>
 * @version $Revision: 1.2 $
 * Created by aschetinin on date Feb 5, 2006 and time 11:25:17 AM
 * Modified by: $Author: zivg $
 * Last updated on: $Date: 2006/02/21 10:41:45 $
public class SynonymsQuery extends Query {

  /** List of QueryInfo objects - the subqueries with all related
information. */
  private ArrayList queries = new ArrayList();

   * Add a custom subquery with specified parameters.
   * This function is intended for using with special fields like
keywords or searchable.
   * @param query the disjunct added
   * @param boost Boost implied to this query scores. Not accounted
during normalization, but only applied to final scores
   * @param synonym Specifies if this query is a synonym or a word
  public void add( Query query, float boost, boolean synonym ) {
    queries.add( new QueryInfo( query, boost, synonym ) );

  /** Returns count of sub-queries */
  public int getSubQueryCount() {
    return queries.size();

   * The Weight for DisjunctionQuery's, used to normalize, score and
explain these queries
   * This class requires two passes in order to properly calculate
weights of its subqueries.
   * On the first pass, it normalizes the keyword field subquery.
   * On the second pass, it normalizes the content field subquery (this
ensures that
   * the ratios in parent queries are normalized to content field).
  private class SynonymsWeight implements Weight {

    /** The searcher with which we are associated. */
    private Searcher searcher;

    /** The Weight's for our subqueries, in 1-1 correspondence with
disjuncts */
    private Weight[] weights;

    /** Sum of squared weights of the original word queries */
    private float sumWordWeights;

    /** Squared weights of the sub-queries */
    private float[] squareWeights;

    /** Count of original word queries */
    private int wordCount;

    /** Count of synonym queries */
    private int synonymCount;

    /** Construct the Weight for this Query searched by searcher.
        Recursively construct subquery weights. */
    public SynonymsWeight( Searcher searcher ) {
      this.searcher = searcher;
      weights = new Weight[queries.size()];
      for( int i = 0; i < queries.size(); i++ )
        weights[i] = ((QueryInfo) queries.get( i
)).getQuery().createWeight( searcher );
      squareWeights = new float[queries.size()];

    /** Return our associated DisjunctionQuery */
    public Query getQuery() {
      return SynonymsQuery.this;

    /** Return our boost */
    public float getValue() {
      return getBoost();

    /** Compute the sub of squared weights of us applied to our
subqueries.  Used for normalization. */
    public float sumOfSquaredWeights() throws IOException {
      sumWordWeights = 0;
      wordCount = 0;
      synonymCount = 0;
      for( int i = 0; i < queries.size(); i++ ) {
        QueryInfo qi = (QueryInfo) queries.get( i );
        squareWeights[i] = weights[i].sumOfSquaredWeights();
        if( qi.getSynonym() ) {
        } else {
          sumWordWeights += weights[i].sumOfSquaredWeights();
      // We ignore IDFs of the synonyms and only use IDF of the original
word for normalization
      return sumWordWeights * getBoost() * getBoost();

    /** Apply the computed normalization factor to our subqueries */
    public void normalize( float norm ) {
      norm *= getBoost();  // Incorporate the query boost (usually 1)
      for( int i = 0; i < queries.size(); i++ ) {
        QueryInfo qi = (QueryInfo) queries.get( i );
        // This is the synonym boost which has to affect the frequency
of synonyms.
        // For full text query:
        // Since tf() = sqrt( freq ), we have to take sqrt of the boost
here in order to get the desired effect.
        // sqrt( boost ) * tf() = sqrt( boost ) * sqrt( freq ) = sqrt(
boost * freq )
        float qnorm = (float) Math.sqrt( qi.getBoost() ) * norm;
        // For synonyms we have to perform two operations:
        if( qi.getSynonym() && (sumWordWeights != 0.0f) &&
(squareWeights[i] != 0.0f) ) {
          // First we multiply the normalization by the relation of
squared weights of the original word and the synonym.
          // This removes the effect of the synonym's IDF and replaces
it with the word IDF when calculating the score.
          // The normalization factor is multiplied by square of IDF,
and this is what we do here -
          // divide on the synonym IDF in order to remove its effect,
and multiply on the original word IDF
          // to add its effect instead of IDF of the synonym.
          qnorm *= (sumWordWeights / squareWeights[i]);
          // Second we apply the relation between IDFs of the synonym
and the orignial word to the term frequency.
          // Since tf() = sqrt( freq ), we have to take sqrt of the
relation here in order to get the desired effect,
          // exactly like with the boost earlier.
          qnorm *= (float) Math.sqrt( squareWeights[i] / sumWordWeights
          // P.S. For full text search these two equations may be easily
turned to one with square root,
          // but I decided to leave them unoptimized for the sake of
        weights[i].normalize( qnorm );

    /** Create the scorer used to score our associated DisjunctionQuery
    public Scorer scorer( IndexReader reader ) throws IOException {
      Similarity sim = SynonymSimilarity.getInstance();
      SynonymScorer result = new SynonymScorer( sim );
      for( int i = 0; i < weights.length; i++ ) {
        Weight w = weights[i];
        Scorer subScorer = w.scorer( reader );
        if( subScorer == null )
          return null;
        result.add( subScorer );
      return result;

    /** Explain the score we computed for doc */
    public Explanation explain( IndexReader reader, int doc ) throws
IOException {
      SynonymSimilarity similarity = SynonymSimilarity.getInstance();
      Explanation result = new Explanation();
      float max = 0.0f, sum = 0.0f;
      result.setDescription( " TF( sum( deTF(weight) )) of:" );
      int numSubQueries = weights.length, numSubQueriesHit = 0;
      for( int i = 0; i < weights.length; i++ ) {
        Explanation e = weights[i].explain( reader, doc );
        if( e.getValue() > 0 ) {
          QueryInfo qi = (QueryInfo) queries.get( i );
          if( qi.getSynonym() && (sumWordWeights != 0.0f) &&
(squareWeights[i] != 0.0f) ) {
            Explanation boostExpl = new Explanation( 0, "synonym
penalty, product of:" );
            float boost = qi.getBoost();
            if( qi.getBoost() != 1 )
              boostExpl.addDetail( new Explanation( qi.getBoost(),
"configuration penalty" ) );
            if( squareWeights[i] != 0.0f ) {
              boostExpl.addDetail( new Explanation( sumWordWeights /
                  "IDF normalization: origWeigth^2=" + sumWordWeights +
" / synonymWeight^2=" + squareWeights[i] ) );
              boost *= (sumWordWeights / squareWeights[i]);
            if( sumWordWeights != 0.0f ) {
              boostExpl.addDetail( new Explanation( (float) Math.sqrt(
squareWeights[i] / sumWordWeights ),
                  "TF normalization: sqrt( synonymWeight^2=" +
squareWeights[i] + " / origWeigth^2=" + sumWordWeights + " )" ) );
              boost *= (float) Math.sqrt( squareWeights[i] /
sumWordWeights );
            boostExpl.setValue( boost );
            e.addDetail( boostExpl );
          } else if( qi.getBoost() != 1 ) { // should never happen since
the original word should always have 1.0 boost
            e.addDetail( new Explanation( qi.getBoost(), "field boost" )
          result.addDetail( e );
          float value = e.getValue();
          sum += similarity.detf( value );
          max = Math.max( max, value );
      sum = sum );
      result.setValue( sum );
      return result;

  }  // end of DisjunctionWeight inner class

  /** Create the Weight used to score us */
  protected Weight createWeight( Searcher searcher ) {
    return new SynonymsWeight( searcher ); // ordered

   * Optimize our representation and our subqueries representations
   * @param reader the IndexReader we query
   * @return an optimized copy of us (which may not be a copy if there
   *         is nothing to optimize)
  public Query rewrite( IndexReader reader ) throws IOException {
    // this query can only be eliminated if it has one subquery without
any modifiers
    if( queries.size() == 1 ) {
      QueryInfo qi = (QueryInfo) queries.get( 0 );
      if( qi.getBoost() == 1 ) { // if they are default
        Query result = qi.getQuery().rewrite( reader );
        if( getBoost() != 1.0f ) {
          if( result == qi.getQuery() )
            result = (Query) result.clone();
          result.setBoost( getBoost() * result.getBoost() );
        return result;
    // otherwise try optimizing the clauses
    SynonymsQuery clone = null;
    for( int i = 0; i < queries.size(); i++ ) {
      QueryInfo clause = (QueryInfo) queries.get( i );
      Query rewrite = clause.getQuery().rewrite( reader );
      if( rewrite != clause.getQuery() ) {
        if( clone == null )
          clone = (SynonymsQuery) this.clone();
        clone.queries.set( i, new QueryInfo( rewrite, clause.getBoost(),
clause.getSynonym() ) );
    if( clone != null )
      return clone;
      return this;

  /* Create a shallow copy of us -- used in rewriting if necessary
   * @return a copy of us (but reuse, don't copy, our subqueries) */
  public Object clone() {
    SynonymsQuery clone = (SynonymsQuery) super.clone();
    clone.queries = (ArrayList) this.queries.clone();
    return clone;

   * Prettyprint us.
   * @param field the field to which we are applied
   * @return a string that shows what we do, of the form "(disjunct1 |
   *         disjunct2 | ... | disjunctn)^boost"
  public String toString( String field ) {
    StringBuffer buffer = new StringBuffer();
    buffer.append( "sum(" );
    for( int i = 0; i < queries.size(); i++ ) {
      QueryInfo qi = (QueryInfo) queries.get( i );
      Query subquery = qi.getQuery();
      //if( subquery instanceof TermQuery )       // wrap sub-bools in
        buffer.append( subquery.toString( field ) );
      //  buffer.append( '(' ).append( subquery.toString( field )
).append( ')' );
      if( qi.getBoost() != 1 )
        buffer.append( '*' ).append( qi.getBoost() );
      if( i != queries.size() - 1 )
        buffer.append( ' ' );
    buffer.append( ')' );
    if( getBoost() != 1.0f ) {
      buffer.append( '^' ).append( getBoost() );
    return buffer.toString();

   * Return true iff we represent the same query as o
   * @param o another object
   * @return true iff o is a DisjunctionQuery with the same boost
   *         and the same subqueries, in the same order, as us
  public boolean equals( Object o ) {
    if( !(o instanceof SynonymsQuery) )
      return false;
    SynonymsQuery other = (SynonymsQuery) o;
    if( this.queries.size() != other.queries.size() )
      return false;
    for( int i = 0; i < this.queries.size(); ++i ) {
      if( !this.queries.get( i ).equals( other.queries.get( i ) ) )
        return false;
    return (this.getBoost() == other.getBoost());

   * Compute a hash code for hashing us
   * @return the hash code
  public int hashCode() {
    return Float.floatToIntBits( getBoost() ) ^ queries.hashCode();

  /** Class containing the information about the specific sub-query */
  private static final class QueryInfo {
    /** Query object */
    private Query query = null;
    /** Boost implied to this query scores. Not accounted during
normalization, but only applied to final scores */
    private float boost = 0;
    /** Specifies if this query is a synonym or a word itself */
    private boolean synonym = false;
    /** Default constructor */
    public QueryInfo( Query query ) {
      this.query = query;
    /** Constructor with all parameters */
    public QueryInfo( Query query, float boost, boolean synonym ) {
      this.query = query;
      this.boost = boost;
      this.synonym = synonym;
    /** Returns the stored query object */
    public Query getQuery() {
      return query;
    /** Returns if this query is a synonym or a word itself */
    public boolean getSynonym() {
      return synonym;
    /** Returns the boost for this query */
    public float getBoost() {
      return boost;
     * Return true iff we represent the same query as o
     * @param o another object
     * @return true iff o is a DisjunctionQuery with the same boost
     *         and the same subqueries, in the same order, as us
    public boolean equals( Object o ) {
      if( !(o instanceof QueryInfo) )
        return false;
      QueryInfo other = (QueryInfo) o;
      return this.query.equals( other.query ) && (this.boost ==
          && (this.synonym == other.synonym);


 * Copyright (c) 2006 Entopia Ltd. All Rights Reserved.


import java.util.ArrayList;
import java.util.Comparator;
import java.util.Arrays;

 * Special scorer for SynonymsQuery query used in full text searches.
 * This scorer does a trick when summarizing the scores of its
 * it applies an opposite function from similarity's tf() function to
get the frequency value back,
 * and then summarizes the values, and re-applies similarity's tf()
function to the sum.
 * This way the scorer makes the sum of scores from several words
behaving like it was one word
 * occuring N times, where N is the sum of occurences of all searched
 * @author <a href="">Andrew S.</a>
 * @version $Revision: 1.2 $
 * Created by aschetinin on date Feb 5, 2006 and time 11:25:17 AM
 * Modified by: $Author: zivg $
 * Last updated on: $Date: 2006/02/21 10:41:45 $
public class SynonymScorer extends Scorer {

  private static final SynonymSimilarity similarity =

   * Creates a new instance of SynonymScorer
   * @param similarity -- not used since our definition involves neither
coord nor terms directly
  public SynonymScorer( Similarity similarity ) {
    super( similarity );

  protected float computeScore(float sum, float max, int coord) {
    return sum;

  /** Look at the class explanation */
  protected float oldScore() throws IOException {
    float max = ((Scorer) subScorers.get(0)).score();
    float sum = similarity.detf( max );
    int numSubQueriesHit = 1;
    for (int i = 1, doc = ((Scorer) subScorers.get(0)).doc(); i <
        subScorers.size() && ((Scorer) subScorers.get(i)).doc() == doc;
i++) {
      float sub = ((Scorer) subScorers.get(i)).score();
      sum += similarity.detf( sub );
      max = Math.max( max, sub );
    sum = similarity.dotf( sum );
    return computeScore( sum, max, numSubQueriesHit );

  /* The scorers for subqueries that have remaining docs, kept sorted
  by number of next doc. */
  protected ArrayList<Scorer> subScorers = new ArrayList<Scorer>();

  private boolean more = false;          // True iff there is a next
  private boolean firstTime = true;      // True iff next() has not yet
been called

  /* Fixed instance of the comparator to reuse */
  private static MaxDisjunctionClauseComparator subScorerComparator =
      new MaxDisjunctionClauseComparator();

  private float currentDocScore = -1F;

  /* Comparator to sort subScorers according to the document number of
  next document */
  private static class MaxDisjunctionClauseComparator implements
      Comparator /*<Scorer>*/ {

    /* Scorers have all been positioned at their next document
  already */
    public int compare(Object /*Scorer*/ s1, Object /*Scorer*/ s2) {
      if (!(s1 instanceof Scorer && s2 instanceof Scorer)) {
        return -1;
      } else {
        return ((Scorer) s1).doc() - ((Scorer) s2).doc();

    /* Compatible equality */
    public boolean equals(Scorer s1, Scorer s2) {
      return s1.doc() == s2.doc();

   * Add the scorer for a subquery
   * @param scorer the scorer of a subquery of our associated
   *               DisjunctionQuery
  public void add(Scorer scorer) throws IOException {
    if ( {       // Initialize and retain only if it
produces docs
      more = true;

   * Generate the next document matching our associated
   * DisjunctionQuery.
   * @return true iff there is a next document
  public boolean next() throws IOException {
    boolean retVal = oldNext();
    if (retVal)
      currentDocScore = oldScore();
    return retVal;

  private boolean oldNext() throws IOException {
    if (!more) return false;
    if (firstTime) {
      return true;   // more would have been false if no subScorers had
any docs
    // Increment all generators that generated the last doc and
incrementally re-sort.
    int lastdoc = subScorers.get( 0 ).doc();
    do {
      if( subScorers.get( 0 ).next() ) {
        Scorer s = subScorers.get( 0 );
        int snextdoc = s.doc(), i = 1;
        for( ; i < subScorers.size() && snextdoc > subScorers.get( i
).doc(); i++ )
          subScorers.set(i - 1, subScorers.get(i));
        if (i != 1) subScorers.set(i - 1, s);
      } else {
        if (subScorers.isEmpty()) return (more = false);
    } while( subScorers.get( 0 ).doc() == lastdoc );
    return true;

  /* First time initialization.  Sort subScorers. */
  private void init() {
    firstTime = false;

  /* Sort subScorers in order of document number of next document to
   be generated */
  private void sortSubScorers() {
    Scorer[] sorted = subScorers.toArray( new Scorer[subScorers.size()]
    Arrays.sort(sorted, subScorerComparator);
    for (int i = 0; i < sorted.length; i++)
      subScorers.set( i, sorted[i] );

   * Determine the current document number.  Initially invalid, until
   * {@link #next()} is called the first time.
   * @return the document number of the currently generated document
  public int doc() {
    return subScorers.get( 0 ).doc();

   * Determine the current document score.  Initially invalid, until
   * {@link #next()} is called the first time.
   * @return the score of the current generated document
  public float score() throws IOException {
    return currentDocScore;

   * Advance to the first document whose number is greater than or
   * equal to target.
   * @param target the minimum number of the next desired document
   * @return true iff there is a document to be generated whose number
   *         is at least target
  public boolean skipTo(int target) throws IOException {
    do {
      if (!next())
        return false;
    } while (target > doc());
    return true;
    // the following, original implementation, erroneously approves docs
    // match on only one subquery
       int i=0;
       while (i<subScorers.size()) {
           if (((Scorer)subScorers.get(i)).skipTo(target)) i++;
           else subScorers.remove(i);
       if (i == 0) return false;
       return true;

   * Explain a score that we computed.  UNSUPPORTED -- see
   * explanation capability in DisjunctionQuery.
   * @param doc the number of a document we scored
   * @return the Explanation for our score
  public Explanation explain(int doc) throws IOException {
    throw new UnsupportedOperationException();



 * Copyright (c) 2006 Entopia Ltd. All Rights Reserved.


 * Special type of similarity used for synonym scoring in full text
 * It returns the term frequency without applying Lucene tf() function
(square root).
 * The tf() function is applied later by the scorer, after summarizing
all synonym scores.
 * @author <a href="">Andrew S.</a>
 * @version $Revision: 1.2 $
 * Created by aschetinin on date Feb 5, 2006 and time 11:25:17 AM
 * Modified by: $Author: zivg $
 * Last updated on: $Date: 2006/02/21 10:41:45 $
public class SynonymSimilarity extends DefaultSimilarity {

  SynonymSimilarity() {

  private static final SynonymSimilarity _instance = new

  public static SynonymSimilarity getInstance() {
    return _instance;

   * do the backward computation to what we did in tf()
  public float detf( float tf ) {
    return tf * tf;

   * we want unaltered term frequence value
  public float tf( float freq ) {
    return freq;

   * the original Lucene tf() function
  public float dotf( float freq ) {
    return (float) Math.sqrt( freq );


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