Java tutorial
/** * Copyright 2004-2005 The Apache Software Foundation. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package stackoverflow.lucene.modified; import gnu.trove.impl.Constants; import gnu.trove.map.hash.TObjectIntHashMap; import java.io.IOException; import java.io.Reader; import java.io.StringReader; import java.util.ArrayList; import java.util.Collection; import java.util.HashMap; import java.util.Map; import java.util.Set; import org.apache.lucene.analysis.Analyzer; import org.apache.lucene.analysis.TokenStream; import org.apache.lucene.analysis.tokenattributes.CharTermAttribute; import org.apache.lucene.document.Document; import org.apache.lucene.index.Fields; import org.apache.lucene.index.IndexReader; import org.apache.lucene.index.IndexableField; import org.apache.lucene.index.MultiFields; import org.apache.lucene.index.Term; import org.apache.lucene.index.Terms; import org.apache.lucene.index.TermsEnum; import org.apache.lucene.search.BooleanClause; import org.apache.lucene.search.BooleanQuery; import org.apache.lucene.search.Query; import org.apache.lucene.search.TermQuery; import org.apache.lucene.util.BytesRef; import org.apache.lucene.util.CharsRef; import org.apache.lucene.util.PriorityQueue; import org.apache.lucene.util.UnicodeUtil; /** * Generate "more like this" similarity queries. * Based on this mail: * <code><pre> * Lucene does let you access the document frequency of terms, with IndexReader.docFreq(). * Term frequencies can be computed by re-tokenizing the text, which, for a single document, * is usually fast enough. But looking up the docFreq() of every term in the document is * probably too slow. * <p/> * You can use some heuristics to prune the set of terms, to avoid calling docFreq() too much, * or at all. Since you're trying to maximize a tf*idf score, you're probably most interested * in terms with a high tf. Choosing a tf threshold even as low as two or three will radically * reduce the number of terms under consideration. Another heuristic is that terms with a * high idf (i.e., a low df) tend to be longer. So you could threshold the terms by the * number of characters, not selecting anything less than, e.g., six or seven characters. * With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms * that do a pretty good job of characterizing a document. * <p/> * It all depends on what you're trying to do. If you're trying to eek out that last percent * of precision and recall regardless of computational difficulty so that you can win a TREC * competition, then the techniques I mention above are useless. But if you're trying to * provide a "more like this" button on a search results page that does a decent job and has * good performance, such techniques might be useful. * <p/> * An efficient, effective "more-like-this" query generator would be a great contribution, if * anyone's interested. I'd imagine that it would take a Reader or a String (the document's * text), analyzer Analyzer, and return a set of representative terms using heuristics like those * above. The frequency and length thresholds could be parameters, etc. * <p/> * Doug * </pre></code> * <p/> * <p/> * <p/> * <h3>Initial Usage</h3> * <p/> * This class has lots of options to try to make it efficient and flexible. * The simplest possible usage is as follows. The bold * fragment is specific to this class. * <p/> * <pre class="prettyprint"> * <p/> * IndexReader ir = ... * IndexSearcher is = ... * <p/> * MoreLikeThis mlt = new MoreLikeThis(ir); * Reader target = ... // orig source of doc you want to find similarities to * Query query = mlt.like( target); * <p/> * Hits hits = is.search(query); * // now the usual iteration thru 'hits' - the only thing to watch for is to make sure * //you ignore the doc if it matches your 'target' document, as it should be similar to itself * <p/> * </pre> * <p/> * Thus you: * <ol> * <li> do your normal, Lucene setup for searching, * <li> create a MoreLikeThis, * <li> get the text of the doc you want to find similarities to * <li> then call one of the like() calls to generate a similarity query * <li> call the searcher to find the similar docs * </ol> * <p/> * <h3>More Advanced Usage</h3> * <p/> * You may want to use {@link #setFieldNames setFieldNames(...)} so you can examine * multiple fields (e.g. body and title) for similarity. * <p/> * <p/> * Depending on the size of your index and the size and makeup of your documents you * may want to call the other set methods to control how the similarity queries are * generated: * <ul> * <li> {@link #setMinTermFreq setMinTermFreq(...)} * <li> {@link #setMinDocFreq setMinDocFreq(...)} * <li> {@link #setMaxDocFreq setMaxDocFreq(...)} * <li> {@link #setMaxDocFreqPct setMaxDocFreqPct(...)} * <li> {@link #setMinWordLen setMinWordLen(...)} * <li> {@link #setMaxWordLen setMaxWordLen(...)} * <li> {@link #setMaxQueryTerms setMaxQueryTerms(...)} * <li> {@link #setMaxNumTokensParsed setMaxNumTokensParsed(...)} * <li> {@link #setStopWords setStopWord(...)} * </ul> * <p/> * <hr> * <pre> * Changes: Mark Harwood 29/02/04 * Some bugfixing, some refactoring, some optimisation. * - bugfix: retrieveTerms(int docNum) was not working for indexes without a termvector -added missing code * - bugfix: No significant terms being created for fields with a termvector - because * was only counting one occurrence per term/field pair in calculations(ie not including frequency info from TermVector) * - refactor: moved common code into isNoiseWord() * - optimise: when no termvector support available - used maxNumTermsParsed to limit amount of tokenization * </pre> */ public final class MoreLikeThis { /** * Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support. * * @see #getMaxNumTokensParsed */ public static final int DEFAULT_MAX_NUM_TOKENS_PARSED = 5000; /** * Ignore terms with less than this frequency in the source doc. * * @see #getMinTermFreq * @see #setMinTermFreq */ public static final int DEFAULT_MIN_TERM_FREQ = 2; /** * Ignore words which do not occur in at least this many docs. * * @see #getMinDocFreq * @see #setMinDocFreq */ public static final int DEFAULT_MIN_DOC_FREQ = 5; /** * Ignore words which occur in more than this many docs. * * @see #getMaxDocFreq * @see #setMaxDocFreq * @see #setMaxDocFreqPct */ public static final int DEFAULT_MAX_DOC_FREQ = Integer.MAX_VALUE; /** * Boost terms in query based on score. * * @see #isBoost * @see #setBoost */ public static final boolean DEFAULT_BOOST = false; /** * Default field names. Null is used to specify that the field names should be looked * up at runtime from the provided reader. */ public static final String[] DEFAULT_FIELD_NAMES = new String[] { "contents" }; /** * Ignore words less than this length or if 0 then this has no effect. * * @see #getMinWordLen * @see #setMinWordLen */ public static final int DEFAULT_MIN_WORD_LENGTH = 0; /** * Ignore words greater than this length or if 0 then this has no effect. * * @see #getMaxWordLen * @see #setMaxWordLen */ public static final int DEFAULT_MAX_WORD_LENGTH = 0; /** * Default set of stopwords. * If null means to allow stop words. * * @see #setStopWords * @see #getStopWords */ public static final Set<?> DEFAULT_STOP_WORDS = null; /** * Current set of stop words. */ private Set<?> stopWords = DEFAULT_STOP_WORDS; /** * Return a Query with no more than this many terms. * * @see BooleanQuery#getMaxClauseCount * @see #getMaxQueryTerms * @see #setMaxQueryTerms */ public static final int DEFAULT_MAX_QUERY_TERMS = 25; /** * Analyzer that will be used to parse the doc. */ private Analyzer analyzer = null; /** * Ignore words less frequent that this. */ private int minTermFreq = DEFAULT_MIN_TERM_FREQ; /** * Ignore words which do not occur in at least this many docs. */ private int minDocFreq = DEFAULT_MIN_DOC_FREQ; /** * Ignore words which occur in more than this many docs. */ private int maxDocFreq = DEFAULT_MAX_DOC_FREQ; /** * Should we apply a boost to the Query based on the scores? */ private boolean boost = DEFAULT_BOOST; /** * Field name we'll analyze. */ private String[] fieldNames = DEFAULT_FIELD_NAMES; /** * The maximum number of tokens to parse in each example doc field that is not stored with TermVector support */ private int maxNumTokensParsed = DEFAULT_MAX_NUM_TOKENS_PARSED; /** * Ignore words if less than this len. */ private int minWordLen = DEFAULT_MIN_WORD_LENGTH; /** * Ignore words if greater than this len. */ private int maxWordLen = DEFAULT_MAX_WORD_LENGTH; /** * Don't return a query longer than this. */ private int maxQueryTerms = DEFAULT_MAX_QUERY_TERMS; /** * For idf() calculations. */ private BM25Similarity similarity;// = new DefaultSimilarity(); private final TObjectIntHashMap<String> freqCache = new TObjectIntHashMap<String>(Constants.DEFAULT_CAPACITY, Constants.DEFAULT_LOAD_FACTOR, -1); /** * IndexReader to use */ private final IndexReader ir; /** * Boost factor to use when boosting the terms */ private float boostFactor = 1; private IndexReader termStatsReader; /** * Returns the boost factor used when boosting terms * * @return the boost factor used when boosting terms * @see #setBoostFactor(float) */ public float getBoostFactor() { return boostFactor; } /** * Sets the boost factor to use when boosting terms * * @see #getBoostFactor() */ public void setBoostFactor(float boostFactor) { this.boostFactor = boostFactor; } /** * Constructor requiring an IndexReader. */ public MoreLikeThis(IndexReader ir, IndexReader termStatsReader) { this(ir, termStatsReader, new BM25Similarity()); } public MoreLikeThis(IndexReader ir, IndexReader termStatsReader, BM25Similarity sim) { this.ir = ir; this.termStatsReader = termStatsReader; this.similarity = sim; } public BM25Similarity getSimilarity() { return similarity; } public void setSimilarity(BM25Similarity similarity) { this.similarity = similarity; } /** * Returns an analyzer that will be used to parse source doc with. The default analyzer * is not set. * * @return the analyzer that will be used to parse source doc with. */ public Analyzer getAnalyzer() { return analyzer; } /** * Sets the analyzer to use. An analyzer is not required for generating a query with the * {@link #like(int)} method, all other 'like' methods require an analyzer. * * @param analyzer the analyzer to use to tokenize text. */ public void setAnalyzer(Analyzer analyzer) { this.analyzer = analyzer; } /** * Returns the frequency below which terms will be ignored in the source doc. The default * frequency is the {@link #DEFAULT_MIN_TERM_FREQ}. * * @return the frequency below which terms will be ignored in the source doc. */ public int getMinTermFreq() { return minTermFreq; } /** * Sets the frequency below which terms will be ignored in the source doc. * * @param minTermFreq the frequency below which terms will be ignored in the source doc. */ public void setMinTermFreq(int minTermFreq) { this.minTermFreq = minTermFreq; } /** * Returns the frequency at which words will be ignored which do not occur in at least this * many docs. The default frequency is {@link #DEFAULT_MIN_DOC_FREQ}. * * @return the frequency at which words will be ignored which do not occur in at least this * many docs. */ public int getMinDocFreq() { return minDocFreq; } /** * Sets the frequency at which words will be ignored which do not occur in at least this * many docs. * * @param minDocFreq the frequency at which words will be ignored which do not occur in at * least this many docs. */ public void setMinDocFreq(int minDocFreq) { this.minDocFreq = minDocFreq; } /** * Returns the maximum frequency in which words may still appear. * Words that appear in more than this many docs will be ignored. The default frequency is * {@link #DEFAULT_MAX_DOC_FREQ}. * * @return get the maximum frequency at which words are still allowed, * words which occur in more docs than this are ignored. */ public int getMaxDocFreq() { return maxDocFreq; } /** * Set the maximum frequency in which words may still appear. Words that appear * in more than this many docs will be ignored. * * @param maxFreq the maximum count of documents that a term may appear * in to be still considered relevant */ public void setMaxDocFreq(int maxFreq) { this.maxDocFreq = maxFreq; } /** * Set the maximum percentage in which words may still appear. Words that appear * in more than this many percent of all docs will be ignored. * * @param maxPercentage the maximum percentage of documents (0-100) that a term may appear * in to be still considered relevant */ public void setMaxDocFreqPct(int maxPercentage) { this.maxDocFreq = maxPercentage * ir.numDocs() / 100; } /** * Returns whether to boost terms in query based on "score" or not. The default is * {@link #DEFAULT_BOOST}. * * @return whether to boost terms in query based on "score" or not. * @see #setBoost */ public boolean isBoost() { return boost; } /** * Sets whether to boost terms in query based on "score" or not. * * @param boost true to boost terms in query based on "score", false otherwise. * @see #isBoost */ public void setBoost(boolean boost) { this.boost = boost; } /** * Returns the field names that will be used when generating the 'More Like This' query. * The default field names that will be used is {@link #DEFAULT_FIELD_NAMES}. * * @return the field names that will be used when generating the 'More Like This' query. */ public String[] getFieldNames() { return fieldNames; } /** * Sets the field names that will be used when generating the 'More Like This' query. * Set this to null for the field names to be determined at runtime from the IndexReader * provided in the constructor. * * @param fieldNames the field names that will be used when generating the 'More Like This' * query. */ public void setFieldNames(String[] fieldNames) { this.fieldNames = fieldNames; } /** * Returns the minimum word length below which words will be ignored. Set this to 0 for no * minimum word length. The default is {@link #DEFAULT_MIN_WORD_LENGTH}. * * @return the minimum word length below which words will be ignored. */ public int getMinWordLen() { return minWordLen; } /** * Sets the minimum word length below which words will be ignored. * * @param minWordLen the minimum word length below which words will be ignored. */ public void setMinWordLen(int minWordLen) { this.minWordLen = minWordLen; } /** * Returns the maximum word length above which words will be ignored. Set this to 0 for no * maximum word length. The default is {@link #DEFAULT_MAX_WORD_LENGTH}. * * @return the maximum word length above which words will be ignored. */ public int getMaxWordLen() { return maxWordLen; } /** * Sets the maximum word length above which words will be ignored. * * @param maxWordLen the maximum word length above which words will be ignored. */ public void setMaxWordLen(int maxWordLen) { this.maxWordLen = maxWordLen; } /** * Set the set of stopwords. * Any word in this set is considered "uninteresting" and ignored. * Even if your Analyzer allows stopwords, you might want to tell the MoreLikeThis code to ignore them, as * for the purposes of document similarity it seems reasonable to assume that "a stop word is never interesting". * * @param stopWords set of stopwords, if null it means to allow stop words * @see #getStopWords */ public void setStopWords(Set<?> stopWords) { this.stopWords = stopWords; } /** * Get the current stop words being used. * * @see #setStopWords */ public Set<?> getStopWords() { return stopWords; } /** * Returns the maximum number of query terms that will be included in any generated query. * The default is {@link #DEFAULT_MAX_QUERY_TERMS}. * * @return the maximum number of query terms that will be included in any generated query. */ public int getMaxQueryTerms() { return maxQueryTerms; } /** * Sets the maximum number of query terms that will be included in any generated query. * * @param maxQueryTerms the maximum number of query terms that will be included in any * generated query. */ public void setMaxQueryTerms(int maxQueryTerms) { this.maxQueryTerms = maxQueryTerms; } /** * @return The maximum number of tokens to parse in each example doc field that is not stored with TermVector support * @see #DEFAULT_MAX_NUM_TOKENS_PARSED */ public int getMaxNumTokensParsed() { return maxNumTokensParsed; } /** * @param i The maximum number of tokens to parse in each example doc field that is not stored with TermVector support */ public void setMaxNumTokensParsed(int i) { maxNumTokensParsed = i; } /** * Return a query that will return docs like the passed lucene document ID. * * @param docNum the documentID of the lucene doc to generate the 'More Like This" query for. * @return a query that will return docs like the passed lucene document ID. */ public Query like(int docNum) throws IOException { if (fieldNames == null) { // gather list of valid fields from lucene Collection<String> fields = MultiFields.getIndexedFields(ir); fieldNames = fields.toArray(new String[fields.size()]); } return createQuery(retrieveTerms(docNum)); } /** * Return a query that will return docs like the passed Reader. * * @return a query that will return docs like the passed Reader. */ public Query like(Reader r, String fieldName) throws IOException { return createQuery(retrieveTerms(r, fieldName)); } /** * Create the More like query from a PriorityQueue */ private Query createQuery(PriorityQueue<Object[]> q) { BooleanQuery query = new BooleanQuery(); Object cur; int qterms = 0; float bestScore = 0; while ((cur = q.pop()) != null) { Object[] ar = (Object[]) cur; TermQuery tq = new TermQuery(new Term((String) ar[1], (String) ar[0])); if (boost) { if (qterms == 0) { bestScore = ((Float) ar[2]); } float myScore = ((Float) ar[2]); tq.setBoost(boostFactor * myScore / bestScore); } try { query.add(tq, BooleanClause.Occur.SHOULD); } catch (BooleanQuery.TooManyClauses ignore) { break; } qterms++; if (maxQueryTerms > 0 && qterms >= maxQueryTerms) { break; } } return query; } /** * Create a PriorityQueue from a word->tf map. * * @param words a map of words keyed on the word(String) with Int objects as the values. */ private PriorityQueue<Object[]> createQueue(Map<String, Int> words) throws IOException { // have collected all words in doc and their freqs int numDocs = ir.numDocs(); FreqQ res = new FreqQ(words.size()); // will order words by score for (String word : words.keySet()) { // for every word int tf = words.get(word).x; // term freq in the source doc if (minTermFreq > 0 && tf < minTermFreq) { continue; // filter out words that don't occur enough times in the source } // go through all the fields and find the largest document frequency int docFreq = 0; for (String fieldName : fieldNames) { int freq = termStatsReader.docFreq(new Term(fieldName, word)); docFreq = (freq > docFreq) ? freq : docFreq; } if (minDocFreq > 0 && docFreq < minDocFreq) { continue; // filter out words that don't occur in enough docs } if (docFreq > maxDocFreq) { continue; // filter out words that occur in too many docs } if (docFreq == 0) { continue; // index update problem? } docFreq = 0; String topField = fieldNames[0]; for (String fieldName : fieldNames) { int freq = ir.docFreq(new Term(fieldName, word)); topField = (freq > docFreq) ? fieldName : topField; docFreq = (freq > docFreq) ? freq : docFreq; } float idf = similarity.idf(docFreq, numDocs); float score = tf * idf; // only really need 1st 3 entries, other ones are for troubleshooting res.insertWithOverflow(new Object[] { word, // the word topField, // the top field score, // overall score idf, // idf docFreq, // freq in all docs tf }); } return res; } /** * Describe the parameters that control how the "more like this" query is formed. */ public String describeParams() { StringBuilder sb = new StringBuilder(); sb.append("\t").append("maxQueryTerms : ").append(maxQueryTerms).append("\n"); sb.append("\t").append("minWordLen : ").append(minWordLen).append("\n"); sb.append("\t").append("maxWordLen : ").append(maxWordLen).append("\n"); sb.append("\t").append("fieldNames : "); String delim = ""; for (String fieldName : fieldNames) { sb.append(delim).append(fieldName); delim = ", "; } sb.append("\n"); sb.append("\t").append("boost : ").append(boost).append("\n"); sb.append("\t").append("minTermFreq : ").append(minTermFreq).append("\n"); sb.append("\t").append("minDocFreq : ").append(minDocFreq).append("\n"); return sb.toString(); } /** * Find words for a more-like-this query former. * * @param docNum the id of the lucene document from which to find terms */ public PriorityQueue<Object[]> retrieveTerms(int docNum) throws IOException { Map<String, Int> termFreqMap = new HashMap<String, Int>(); for (String fieldName : fieldNames) { final Fields vectors = ir.getTermVectors(docNum); final Terms vector; if (vectors != null) { vector = vectors.terms(fieldName); } else { vector = null; } // field does not store term vector info if (vector == null) { Document d = ir.document(docNum); IndexableField fields[] = d.getFields(fieldName); for (IndexableField field : fields) { final String stringValue = field.stringValue(); if (stringValue != null) { addTermFrequencies(new StringReader(stringValue), termFreqMap, fieldName); } } } else { addTermFrequencies(termFreqMap, vector); } } return createQueue(termFreqMap); } /** * Adds terms and frequencies found in vector into the Map termFreqMap * * @param termFreqMap a Map of terms and their frequencies * @param vector List of terms and their frequencies for a doc/field */ private void addTermFrequencies(Map<String, Int> termFreqMap, Terms vector) throws IOException { final TermsEnum termsEnum = vector.iterator(null); final CharsRef spare = new CharsRef(); BytesRef text; while ((text = termsEnum.next()) != null) { UnicodeUtil.UTF8toUTF16(text, spare); final String term = spare.toString(); if (isNoiseWord(term)) { continue; } final int freq = (int) termsEnum.totalTermFreq(); // increment frequency Int cnt = termFreqMap.get(term); if (cnt == null) { cnt = new Int(); termFreqMap.put(term, cnt); cnt.x = freq; } else { cnt.x += freq; } } } /** * Adds term frequencies found by tokenizing text from reader into the Map words * * @param r a source of text to be tokenized * @param termFreqMap a Map of terms and their frequencies * @param fieldName Used by analyzer for any special per-field analysis */ private void addTermFrequencies(Reader r, Map<String, Int> termFreqMap, String fieldName) throws IOException { if (analyzer == null) { throw new UnsupportedOperationException( "To use MoreLikeThis without " + "term vectors, you must provide an Analyzer"); } TokenStream ts = analyzer.tokenStream(fieldName, r); int tokenCount = 0; // for every token CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); while (ts.incrementToken()) { String word = termAtt.toString(); tokenCount++; if (tokenCount > maxNumTokensParsed) { break; } if (isNoiseWord(word)) { continue; } // increment frequency Int cnt = termFreqMap.get(word); if (cnt == null) { termFreqMap.put(word, new Int()); } else { cnt.x++; } } ts.end(); ts.close(); } /** * determines if the passed term is likely to be of interest in "more like" comparisons * * @param term The word being considered * @return true if should be ignored, false if should be used in further analysis */ private boolean isNoiseWord(String term) { int len = term.length(); if (minWordLen > 0 && len < minWordLen) { return true; } if (maxWordLen > 0 && len > maxWordLen) { return true; } return stopWords != null && stopWords.contains(term); } /** * Find words for a more-like-this query former. * The result is a priority queue of arrays with one entry for <b>every word</b> in the document. * Each array has 6 elements. * The elements are: * <ol> * <li> The word (String) * <li> The top field that this word comes from (String) * <li> The score for this word (Float) * <li> The IDF value (Float) * <li> The frequency of this word in the index (Integer) * <li> The frequency of this word in the source document (Integer) * </ol> * This is a somewhat "advanced" routine, and in general only the 1st entry in the array is of interest. * This method is exposed so that you can identify the "interesting words" in a document. * For an easier method to call see {@link #retrieveInterestingTerms retrieveInterestingTerms()}. * * @param r the reader that has the content of the document * @param fieldName field passed to the analyzer to use when analyzing the content * @return the most interesting words in the document ordered by score, with the highest scoring, or best entry, first * @see #retrieveInterestingTerms */ public PriorityQueue<Object[]> retrieveTerms(Reader r, String fieldName) throws IOException { Map<String, Int> words = new HashMap<String, Int>(); addTermFrequencies(r, words, fieldName); return createQueue(words); } /** * @see #retrieveInterestingTerms(java.io.Reader, String) */ public String[] retrieveInterestingTerms(int docNum) throws IOException { ArrayList<Object> al = new ArrayList<Object>(maxQueryTerms); PriorityQueue<Object[]> pq = retrieveTerms(docNum); Object cur; int lim = maxQueryTerms; // have to be careful, retrieveTerms returns all words but that's probably not useful to our caller... // we just want to return the top words while (((cur = pq.pop()) != null) && lim-- > 0) { Object[] ar = (Object[]) cur; al.add(ar[0]); // the 1st entry is the interesting word } String[] res = new String[al.size()]; return al.toArray(res); } /** * Convenience routine to make it easy to return the most interesting words in a document. * More advanced users will call {@link #retrieveTerms(Reader, String) retrieveTerms()} directly. * * @param r the source document * @param fieldName field passed to analyzer to use when analyzing the content * @return the most interesting words in the document * @see #retrieveTerms(java.io.Reader, String) * @see #setMaxQueryTerms */ public String[] retrieveInterestingTerms(Reader r, String fieldName) throws IOException { ArrayList<Object> al = new ArrayList<Object>(maxQueryTerms); PriorityQueue<Object[]> pq = retrieveTerms(r, fieldName); Object cur; int lim = maxQueryTerms; // have to be careful, retrieveTerms returns all words but that's probably not useful to our caller... // we just want to return the top words while (((cur = pq.pop()) != null) && lim-- > 0) { Object[] ar = (Object[]) cur; al.add(ar[0]); // the 1st entry is the interesting word } String[] res = new String[al.size()]; return al.toArray(res); } /** * PriorityQueue that orders words by score. */ private static class FreqQ extends PriorityQueue<Object[]> { FreqQ(int s) { super(s); } @Override protected boolean lessThan(Object[] aa, Object[] bb) { Float fa = (Float) aa[2]; Float fb = (Float) bb[2]; return fa > fb; } } /** * Use for frequencies and to avoid renewing Integers. */ private static class Int { int x; Int() { x = 1; } } }