Package org.apache.jackrabbit.core.query.lucene

Class MoreLikeThis

  • public final class MoreLikeThis
extends Object
    Generate "more like this" similarity queries. Based on this mail: 
     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.

 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.

 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.

 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.

 Doug

    Initial Usage

    This class has lots of options to try to make it efficient and flexible. 
    
 IndexReader ir = ...
 IndexSearcher is = ...
 
 MoreLikeThis mlt = new MoreLikeThis(ir);
 Reader target = ... // orig source of doc you want to find similarities to
 Query query = mlt.like( target);
 
 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
    Thus you:
    1. do your normal, Lucene setup for searching,
    2. create a MoreLikeThis,
    3. get the text of the doc you want to find similaries to
    4. then call one of the like() calls to generate a similarity query
    5. call the searcher to find the similar docs

    More Advanced Usage

    You may want to use setFieldNames(...) so you can examine multiple fields (e.g. body and title) for similarity.

    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: 

     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 occurence 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

    • Constructor Detail

      • MoreLikeThis

        public MoreLikeThis​(IndexReader ir)
        Constructor requiring an IndexReader.

    • Method Detail

      • getSimilarity

        public Similarity getSimilarity()

      • setSimilarity

        public void setSimilarity​(Similarity similarity)

      • getAnalyzer

        public Analyzer getAnalyzer()
        Returns an analyzer that will be used to parse source doc with. The default analyzer is the DEFAULT_ANALYZER.
        Returns:
        the analyzer that will be used to parse source doc with.
        See Also:
        DEFAULT_ANALYZER

      • setAnalyzer

        public void setAnalyzer​(Analyzer analyzer)
        Sets the analyzer to use. An analyzer is not required for generating a query with the like(int) method, all other 'like' methods require an analyzer.
        Parameters:
        analyzer - the analyzer to use to tokenize text.

      • getMinTermFreq

        public int getMinTermFreq()
        Returns the frequency below which terms will be ignored in the source doc. The default frequency is the DEFAULT_MIN_TERM_FREQ.
        Returns:
        the frequency below which terms will be ignored in the source doc.

      • setMinTermFreq

        public void setMinTermFreq​(int minTermFreq)
        Sets the frequency below which terms will be ignored in the source doc.
        Parameters:
        minTermFreq - the frequency below which terms will be ignored in the source doc.

      • getMinDocFreq

        public int getMinDocFreq()
        Returns the frequency at which words will be ignored which do not occur in at least this many docs. The default frequency is DEFAULT_MIN_DOC_FREQ.
        Returns:
        the frequency at which words will be ignored which do not occur in at least this many docs.

      • setMinDocFreq

        public void setMinDocFreq​(int minDocFreq)
        Sets the frequency at which words will be ignored which do not occur in at least this many docs.
        Parameters:
        minDocFreq - the frequency at which words will be ignored which do not occur in at least this many docs.

      • isBoost

        public boolean isBoost()
        Returns whether to boost terms in query based on "score" or not. The default is DEFAULT_BOOST.
        Returns:
        whether to boost terms in query based on "score" or not.
        See Also:
        setBoost(boolean)

      • setBoost

        public void setBoost​(boolean boost)
        Sets whether to boost terms in query based on "score" or not.
        Parameters:
        boost - true to boost terms in query based on "score", false otherwise.
        See Also:
        isBoost()

      • getFieldNames

        public String[] getFieldNames()
        Returns the field names that will be used when generating the 'More Like This' query. The default field names that will be used is DEFAULT_FIELD_NAMES.
        Returns:
        the field names that will be used when generating the 'More Like This' query.

      • setFieldNames

        public void setFieldNames​(String[] 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.
        Parameters:
        fieldNames - the field names that will be used when generating the 'More Like This' query.

      • getMinWordLen

        public int getMinWordLen()
        Returns the minimum word length below which words will be ignored. Set this to 0 for no minimum word length. The default is DEFAULT_MIN_WORD_LENGTH.
        Returns:
        the minimum word length below which words will be ignored.

      • setMinWordLen

        public void setMinWordLen​(int minWordLen)
        Sets the minimum word length below which words will be ignored.
        Parameters:
        minWordLen - the minimum word length below which words will be ignored.

      • getMaxWordLen

        public int getMaxWordLen()
        Returns the maximum word length above which words will be ignored. Set this to 0 for no maximum word length. The default is DEFAULT_MAX_WORD_LENGTH.
        Returns:
        the maximum word length above which words will be ignored.

      • setMaxWordLen

        public void setMaxWordLen​(int maxWordLen)
        Sets the maximum word length above which words will be ignored.
        Parameters:
        maxWordLen - the maximum word length above which words will be ignored.

      • setStopWords

        public void setStopWords​(Set<String> stopWords)
        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".
        Parameters:
        stopWords - set of stopwords, if null it means to allow stop words
        See Also:
        StopFilter.makeStopSet(), getStopWords()

      • getMaxQueryTerms

        public int getMaxQueryTerms()
        Returns the maximum number of query terms that will be included in any generated query. The default is DEFAULT_MAX_QUERY_TERMS.
        Returns:
        the maximum number of query terms that will be included in any generated query.

      • setMaxQueryTerms

        public void setMaxQueryTerms​(int maxQueryTerms)
        Sets the maximum number of query terms that will be included in any generated query.
        Parameters:
        maxQueryTerms - the maximum number of query terms that will be included in any generated query.

      • getMaxNumTokensParsed

        public int getMaxNumTokensParsed()
        Returns:
        The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
        See Also:
        DEFAULT_MAX_NUM_TOKENS_PARSED

      • setMaxNumTokensParsed

        public void setMaxNumTokensParsed​(int i)
        Parameters:
        i - The maximum number of tokens to parse in each example doc field that is not stored with TermVector support

      • like

        public Query like​(int docNum)
           throws IOException
        Return a query that will return docs like the passed lucene document ID.
        Parameters:
        docNum - the documentID of the lucene doc to generate the 'More Like This" query for.
        Returns:
        a query that will return docs like the passed lucene document ID.
        Throws:
        IOException

      • like

        public Query like​(File f)
           throws IOException
        Return a query that will return docs like the passed file.
        Returns:
        a query that will return docs like the passed file.
        Throws:
        IOException

      • like

        public Query like​(URL u)
           throws IOException
        Return a query that will return docs like the passed URL.
        Returns:
        a query that will return docs like the passed URL.
        Throws:
        IOException

      • like

        public Query like​(InputStream is)
           throws IOException
        Return a query that will return docs like the passed stream.
        Returns:
        a query that will return docs like the passed stream.
        Throws:
        IOException

      • like

        public Query like​(Reader r)
           throws IOException
        Return a query that will return docs like the passed Reader.
        Returns:
        a query that will return docs like the passed Reader.
        Throws:
        IOException

      • describeParams

        public String describeParams()
        Describe the parameters that control how the "more like this" query is formed.

      • retrieveTerms

        public PriorityQueue retrieveTerms​(int docNum)
                            throws IOException
        Find words for a more-like-this query former.
        Parameters:
        docNum - the id of the lucene document from which to find terms
        Throws:
        IOException

      • retrieveTerms

        public PriorityQueue retrieveTerms​(Reader r)
                            throws IOException
        Find words for a more-like-this query former. The result is a priority queue of arrays with one entry for every word in the document. Each array has 6 elements. The elements are:
        1. The word (String)
        2. The top field that this word comes from (String)
        3. The score for this word (Float)
        4. The IDF value (Float)
        5. The frequency of this word in the index (Integer)
        6. The frequency of this word in the source document (Integer)
        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 retrieveInterestingTerms().
        Parameters:
        r - the reader that has the content of the document
        Returns:
        the most interesting words in the document ordered by score, with the highest scoring, or best entry, first
        Throws:
        IOException
        See Also:
        retrieveInterestingTerms(int)