org.apache.lucene.search.similar

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. See the body of {@link #main 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 {@link #setFieldNames 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
 

Author: David Spencer Bruce Ritchie Mark Harwood

Field Summary
static intDEFALT_MIN_DOC_FREQ
Ignore words which do not occur in at least this many docs.
static AnalyzerDEFAULT_ANALYZER
Default analyzer to parse source doc with.
static booleanDEFAULT_BOOST
Boost terms in query based on score.
static String[]DEFAULT_FIELD_NAMES
Default field names.
static intDEFAULT_MAX_NUM_TOKENS_PARSED
Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
static intDEFAULT_MAX_QUERY_TERMS
Return a Query with no more than this many terms.
static intDEFAULT_MAX_WORD_LENGTH
Ignore words greater than this length or if 0 then this has no effect.
static intDEFAULT_MIN_TERM_FREQ
Ignore terms with less than this frequency in the source doc.
static intDEFAULT_MIN_WORD_LENGTH
Ignore words less than this length or if 0 then this has no effect.
static SetDEFAULT_STOP_WORDS
Default set of stopwords.
Constructor Summary
MoreLikeThis(IndexReader ir)
Constructor requiring an IndexReader.
Method Summary
StringdescribeParams()
Describe the parameters that control how the "more like this" query is formed.
AnalyzergetAnalyzer()
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.
intgetMaxNumTokensParsed()
intgetMaxQueryTerms()
Returns the maximum number of query terms that will be included in any generated query.
intgetMaxWordLen()
Returns the maximum word length above which words will be ignored.
intgetMinDocFreq()
Returns the frequency at which words will be ignored which do not occur in at least this many docs.
intgetMinTermFreq()
Returns the frequency below which terms will be ignored in the source doc.
intgetMinWordLen()
Returns the minimum word length below which words will be ignored.
SetgetStopWords()
Get the current stop words being used.
booleanisBoost()
Returns whether to boost terms in query based on "score" or not.
Querylike(int docNum)
Return a query that will return docs like the passed lucene document ID.
Querylike(File f)
Return a query that will return docs like the passed file.
Querylike(URL u)
Return a query that will return docs like the passed URL.
Querylike(InputStream is)
Return a query that will return docs like the passed stream.
Querylike(Reader r)
Return a query that will return docs like the passed Reader.
static voidmain(String[] a)
Test driver.
String[]retrieveInterestingTerms(Reader r)
Convenience routine to make it easy to return the most interesting words in a document.
PriorityQueueretrieveTerms(Reader r)
Find words for a more-like-this query former.
voidsetAnalyzer(Analyzer analyzer)
Sets the analyzer to use.
voidsetBoost(boolean boost)
Sets whether to boost terms in query based on "score" or not.
voidsetFieldNames(String[] fieldNames)
Sets the field names that will be used when generating the 'More Like This' query.
voidsetMaxNumTokensParsed(int i)
voidsetMaxQueryTerms(int maxQueryTerms)
Sets the maximum number of query terms that will be included in any generated query.
voidsetMaxWordLen(int maxWordLen)
Sets the maximum word length above which words will be ignored.
voidsetMinDocFreq(int minDocFreq)
Sets the frequency at which words will be ignored which do not occur in at least this many docs.
voidsetMinTermFreq(int minTermFreq)
Sets the frequency below which terms will be ignored in the source doc.
voidsetMinWordLen(int minWordLen)
Sets the minimum word length below which words will be ignored.
voidsetStopWords(Set stopWords)
Set the set of stopwords.

Field Detail

DEFALT_MIN_DOC_FREQ

public static final int DEFALT_MIN_DOC_FREQ
Ignore words which do not occur in at least this many docs.

See Also: MoreLikeThis MoreLikeThis

DEFAULT_ANALYZER

public static final Analyzer DEFAULT_ANALYZER
Default analyzer to parse source doc with.

See Also: MoreLikeThis

DEFAULT_BOOST

public static final boolean DEFAULT_BOOST
Boost terms in query based on score.

See Also: MoreLikeThis MoreLikeThis

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_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: MoreLikeThis

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 MoreLikeThis MoreLikeThis

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: MoreLikeThis MoreLikeThis

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: MoreLikeThis MoreLikeThis

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: MoreLikeThis MoreLikeThis

DEFAULT_STOP_WORDS

public static final Set DEFAULT_STOP_WORDS
Default set of stopwords. If null means to allow stop words.

See Also: MoreLikeThis MoreLikeThis

Constructor Detail

MoreLikeThis

public MoreLikeThis(IndexReader ir)
Constructor requiring an IndexReader.

Method Detail

describeParams

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

getAnalyzer

public Analyzer getAnalyzer()
Returns an analyzer that will be used to parse source doc with. The default analyzer is the {@link #DEFAULT_ANALYZER}.

Returns: the analyzer that will be used to parse source doc with.

See Also: DEFAULT_ANALYZER

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 {@link #DEFAULT_FIELD_NAMES}.

Returns: the field names that will be used when generating the 'More Like This' 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

getMaxQueryTerms

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

Returns: the maximum number of query terms that will be included in any generated query.

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 {@link #DEFAULT_MAX_WORD_LENGTH}.

Returns: the maximum word length above which words will be ignored.

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 {@link #DEFALT_MIN_DOC_FREQ}.

Returns: the frequency at which words will be ignored which do not occur in at least this many docs.

getMinTermFreq

public int getMinTermFreq()
Returns the frequency below which terms will be ignored in the source doc. The default frequency is the {@link #DEFAULT_MIN_TERM_FREQ}.

Returns: the frequency below which terms will be ignored in the source doc.

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 {@link #DEFAULT_MIN_WORD_LENGTH}.

Returns: the minimum word length below which words will be ignored.

getStopWords

public Set getStopWords()
Get the current stop words being used.

See Also: MoreLikeThis

isBoost

public boolean isBoost()
Returns whether to boost terms in query based on "score" or not. The default is {@link #DEFAULT_BOOST}.

Returns: whether to boost terms in query based on "score" or not.

See Also: MoreLikeThis

like

public Query like(int docNum)
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.

like

public Query like(File f)
Return a query that will return docs like the passed file.

Returns: a query that will return docs like the passed file.

like

public Query like(URL u)
Return a query that will return docs like the passed URL.

Returns: a query that will return docs like the passed URL.

like

public Query like(InputStream is)
Return a query that will return docs like the passed stream.

Returns: a query that will return docs like the passed stream.

like

public Query like(Reader r)
Return a query that will return docs like the passed Reader.

Returns: a query that will return docs like the passed Reader.

main

public static void main(String[] a)
Test driver. Pass in "-i INDEX" and then either "-fn FILE" or "-url URL".

retrieveInterestingTerms

public String[] retrieveInterestingTerms(Reader r)
Convenience routine to make it easy to return the most interesting words in a document. More advanced users will call {@link #retrieveTerms(java.io.Reader) retrieveTerms()} directly.

Parameters: r the source document

Returns: the most interesting words in the document

See Also: retrieveTerms MoreLikeThis

retrieveTerms

public PriorityQueue retrieveTerms(Reader r)
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 {@link #retrieveInterestingTerms retrieveInterestingTerms()}.

Parameters: r the reader that has the content of the document

Returns: the most intresting words in the document ordered by score, with the highest scoring, or best entry, first

See Also: MoreLikeThis

setAnalyzer

public void setAnalyzer(Analyzer 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.

Parameters: analyzer the analyzer to use to tokenize text.

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: MoreLikeThis

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.

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

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.

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.

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.

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.

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.

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() MoreLikeThis

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