MoreLikeThis (Lucene 2.4.1 API)
Keywords: org.apache.lucene.search.similar.MoreLikeThis class

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## org.apache.lucene.search.similar
Class MoreLikeThis

    java.lang.Object
      org.apache.lucene.search.similar.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. See the body of `main()` below in the source for real code, or if you want pseudo code, the simpliest possible usage is as follows. The bold fragment is specific to this class. `

     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:

- `setMinTermFreq(...)`
- `setMinDocFreq(...)`
- `setMinWordLen(...)`
- `setMaxWordLen(...)`
- `setMaxQueryTerms(...)`
- `setMaxNumTokensParsed(...)`
- `setStopWord(...)`

     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

Field Summary
`static Analyzer` | `DEFAULT_ANALYZER` Default analyzer to parse source doc with.
`static boolean` | `DEFAULT_BOOST` Boost terms in query based on score.
`static String[]` | `DEFAULT_FIELD_NAMES` Default field names.
`static int` | `DEFAULT_MAX_NUM_TOKENS_PARSED` Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
`static int` | `DEFAULT_MAX_QUERY_TERMS` Return a Query with no more than this many terms.
`static int` | `DEFAULT_MAX_WORD_LENGTH` Ignore words greater than this length or if 0 then this has no effect.
`static int` | `DEFAULT_MIN_DOC_FREQ` Ignore words which do not occur in at least this many docs.
`static int` | `DEFAULT_MIN_TERM_FREQ` Ignore terms with less than this frequency in the source doc.
`static int` | `DEFAULT_MIN_WORD_LENGTH` Ignore words less than this length or if 0 then this has no effect.
`static Set` | `DEFAULT_STOP_WORDS` Default set of stopwords.

Constructor Summary
`MoreLikeThis(IndexReader ir)` Constructor requiring an IndexReader.
`MoreLikeThis(IndexReader ir, Similarity sim)`

Method Summary
`String` | `describeParams()` Describe the parameters that control how the "more like this" query is formed.
`Analyzer` | `getAnalyzer()` Returns an analyzer that will be used to parse source doc with.
`String[]` | `getFieldNames()` Returns the field names that will be used when generating the 'More Like This' query.
`int` | `getMaxNumTokensParsed()`
`int` | `getMaxQueryTerms()` Returns the maximum number of query terms that will be included in any generated query.
`int` | `getMaxWordLen()` Returns the maximum word length above which words will be ignored.
`int` | `getMinDocFreq()` Returns the frequency at which words will be ignored which do not occur in at least this many docs.
`int` | `getMinTermFreq()` Returns the frequency below which terms will be ignored in the source doc.
`int` | `getMinWordLen()` Returns the minimum word length below which words will be ignored.
`Similarity` | `getSimilarity()`
`Set` | `getStopWords()` Get the current stop words being used.
`boolean` | `isBoost()` Returns whether to boost terms in query based on "score" or not.
`Query` | `like(File f)` Return a query that will return docs like the passed file.
`Query` | `like(InputStream is)` Return a query that will return docs like the passed stream.
`Query` | `like(int docNum)` Return a query that will return docs like the passed lucene document ID.
`Query` | `like(Reader r)` Return a query that will return docs like the passed Reader.
`Query` | `like(URL u)` Return a query that will return docs like the passed URL.
`static void` | `main(String[] a)` Test driver.
`String[]` | `retrieveInterestingTerms(int docNum)`
`String[]` | `retrieveInterestingTerms(Reader r)` Convenience routine to make it easy to return the most interesting words in a document.
`PriorityQueue` | `retrieveTerms(int docNum)` Find words for a more-like-this query former.
`PriorityQueue` | `retrieveTerms(Reader r)` Find words for a more-like-this query former.
`void` | `setAnalyzer(Analyzer analyzer)` Sets the analyzer to use.
`void` | `setBoost(boolean boost)` Sets whether to boost terms in query based on "score" or not.
`void` | `setFieldNames(String[] fieldNames)` Sets the field names that will be used when generating the 'More Like This' query.
`void` | `setMaxNumTokensParsed(int i)`
`void` | `setMaxQueryTerms(int maxQueryTerms)` Sets the maximum number of query terms that will be included in any generated query.
`void` | `setMaxWordLen(int maxWordLen)` Sets the maximum word length above which words will be ignored.
`void` | `setMinDocFreq(int minDocFreq)` Sets the frequency at which words will be ignored which do not occur in at least this many docs.
`void` | `setMinTermFreq(int minTermFreq)` Sets the frequency below which terms will be ignored in the source doc.
`void` | `setMinWordLen(int minWordLen)` Sets the minimum word length below which words will be ignored.
`void` | `setSimilarity(Similarity similarity)`
`void` | `setStopWords(Set stopWords)` Set the set of stopwords.

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### DEFAULT_MAX_NUM_TOKENS_PARSED

    public static final int DEFAULT_MAX_NUM_TOKENS_PARSED

  Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.

  See Also:
    `getMaxNumTokensParsed()`, Constant Field Values

### DEFAULT_ANALYZER

    public static final Analyzer DEFAULT_ANALYZER

  Default analyzer to parse source doc with.

  See Also:
    `getAnalyzer()`

### DEFAULT_MIN_TERM_FREQ

    public static final int DEFAULT_MIN_TERM_FREQ

  Ignore terms with less than this frequency in the source doc.

  See Also:
    `getMinTermFreq()`, `setMinTermFreq(int)`, Constant Field Values

### DEFAULT_MIN_DOC_FREQ

    public static final int DEFAULT_MIN_DOC_FREQ

  Ignore words which do not occur in at least this many docs.

  See Also:
    `getMinDocFreq()`, `setMinDocFreq(int)`, Constant Field Values

### DEFAULT_BOOST

    public static final boolean DEFAULT_BOOST

  Boost terms in query based on score.

  See Also:
    `isBoost()`, `setBoost(boolean)`, Constant Field Values

### DEFAULT_FIELD_NAMES

    public static final String[] DEFAULT_FIELD_NAMES

  Default field names. Null is used to specify that the field names should be looked up at runtime from the provided reader.

### DEFAULT_MIN_WORD_LENGTH

    public static final int DEFAULT_MIN_WORD_LENGTH

  Ignore words less than this length or if 0 then this has no effect.

  See Also:
    `getMinWordLen()`, `setMinWordLen(int)`, Constant Field Values

### DEFAULT_MAX_WORD_LENGTH

    public static final int DEFAULT_MAX_WORD_LENGTH

  Ignore words greater than this length or if 0 then this has no effect.

  See Also:
    `getMaxWordLen()`, `setMaxWordLen(int)`, Constant Field Values

### DEFAULT_STOP_WORDS

    public static final Set DEFAULT_STOP_WORDS

  Default set of stopwords. If null means to allow stop words.

  See Also:
    `setStopWords(java.util.Set)`, `getStopWords()`

### DEFAULT_MAX_QUERY_TERMS

    public static final int DEFAULT_MAX_QUERY_TERMS

  Return a Query with no more than this many terms.

  See Also:
    `BooleanQuery.getMaxClauseCount()`, `getMaxQueryTerms()`, `setMaxQueryTerms(int)`, Constant Field Values

Constructor Detail

### MoreLikeThis

    public MoreLikeThis(IndexReader ir)

  Constructor requiring an IndexReader.

### MoreLikeThis

    public MoreLikeThis(IndexReader ir,
                        Similarity sim)

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 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()`

### getStopWords

    public Set getStopWords()

  Get the current stop words being used.

  See Also:
    `setStopWords(java.util.Set)`

### 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.

### main

    public static void main(String[] a)
                     throws Throwable

  Test driver. Pass in "-i INDEX" and then either "-fn FILE" or "-url URL".

  Throws:
    `Throwable`

### 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)`

### retrieveInterestingTerms

    public String[] retrieveInterestingTerms(int docNum)
                                      throws IOException

  Throws:
    `IOException`
  See Also:
    `retrieveInterestingTerms(java.io.Reader)`

### retrieveInterestingTerms

    public String[] retrieveInterestingTerms(Reader r)
                                      throws IOException

  Convenience routine to make it easy to return the most interesting words in a document. More advanced users will call `retrieveTerms()` directly.

  Parameters:
    `r` - the source document
  Returns:
    the most interesting words in the document
  Throws:
    `IOException`
  See Also:
    `retrieveTerms(java.io.Reader)`, `setMaxQueryTerms(int)`

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