Class MoreLikeThis

Generate "more like this" similarity queries. Based on this mail:

Lucene does let you access the document frequency of terms, with DocFreq(Term).
Term frequencies can be computed by re-tokenizing the text, which, for a single document,
is usually fast enough.  But looking up the DocFreq(Term) 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(Term) 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. The simplest possible usage is as follows. The bold fragment is specific to this class.

IndexReader ir = ...
IndexSearcher is = ...

MoreLikeThis mlt = new MoreLikeThis(ir);
TextReader 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:

• do your normal, Lucene setup for searching,
• create a MoreLikeThis,
• get the text of the doc you want to find similarities to
• then call one of the Like(TextReader, String) calls to generate a similarity query
• call the searcher to find the similar docs

More Advanced Usage

You may want to use the setter for FieldNames 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:

• MinTermFreq
• MinDocFreq
• MaxDocFreq
• SetMaxDocFreqPct(Int32)
• MinWordLen
• MaxWordLen
• MaxQueryTerms
• MaxNumTokensParsed
• StopWords

Inheritance

System.Object

MoreLikeThis

Namespace: Lucene.Net.Queries.Mlt

Assembly: Lucene.Net.Queries.dll

Syntax

public sealed class MoreLikeThis : object

Remarks

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

Constructors

MoreLikeThis(IndexReader)

Constructor requiring an IndexReader.

Declaration

public MoreLikeThis(IndexReader ir)

Parameters

Type	Name	Description
IndexReader	ir

MoreLikeThis(IndexReader, TFIDFSimilarity)

Declaration

public MoreLikeThis(IndexReader ir, TFIDFSimilarity sim)

Parameters

Type	Name	Description
IndexReader	ir
TFIDFSimilarity	sim

Fields

DEFAULT_BOOST

Boost terms in query based on score.

Declaration

public static readonly bool DEFAULT_BOOST

Field Value

Type	Description
System.Boolean

See Also

ApplyBoost

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.

Declaration

public static readonly string[] DEFAULT_FIELD_NAMES

Field Value

Type	Description
System.String[]

DEFAULT_MAX_DOC_FREQ

Ignore words which occur in more than this many docs.

Declaration

public static readonly int DEFAULT_MAX_DOC_FREQ

Field Value

Type	Description
System.Int32

See Also

MaxDocFreq

SetMaxDocFreqPct(Int32)

DEFAULT_MAX_NUM_TOKENS_PARSED

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

Declaration

public static readonly int DEFAULT_MAX_NUM_TOKENS_PARSED

Field Value

Type	Description
System.Int32

See Also

MaxNumTokensParsed

DEFAULT_MAX_QUERY_TERMS

Return a Query with no more than this many terms.

Declaration

public static readonly int DEFAULT_MAX_QUERY_TERMS

Field Value

Type	Description
System.Int32

See Also

MaxClauseCount

MaxQueryTerms

DEFAULT_MAX_WORD_LENGTH

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

Declaration

public static readonly int DEFAULT_MAX_WORD_LENGTH

Field Value

Type	Description
System.Int32

See Also

MaxWordLen

DEFAULT_MIN_DOC_FREQ

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

Declaration

public static readonly int DEFAULT_MIN_DOC_FREQ

Field Value

Type	Description
System.Int32

See Also

MinDocFreq

DEFAULT_MIN_TERM_FREQ

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

Declaration

public static readonly int DEFAULT_MIN_TERM_FREQ

Field Value

Type	Description
System.Int32

See Also

MinTermFreq

DEFAULT_MIN_WORD_LENGTH

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

Declaration

public static readonly int DEFAULT_MIN_WORD_LENGTH

Field Value

Type	Description
System.Int32

See Also

MinWordLen

DEFAULT_STOP_WORDS

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

Declaration

public static readonly ISet<string> DEFAULT_STOP_WORDS

Field Value

Type	Description
ISet<System.String>

See Also

StopWords

Properties

Analyzer

Gets or Sets an analyzer that will be used to parse source doc with. The default analyzer is not set. An analyzer is not required for generating a query with the Like(Int32) method, all other 'like' methods require an analyzer.

Declaration

public Analyzer Analyzer { get; set; }

Property Value

Type	Description
Analyzer

ApplyBoost

Gets or Sets whether to boost terms in query based on "score" or not. The default is DEFAULT_BOOST.

Declaration

public bool ApplyBoost { get; set; }

Property Value

Type	Description
System.Boolean

BoostFactor

Gets or Sets the boost factor used when boosting terms

Declaration

public float BoostFactor { get; set; }

Property Value

Type	Description
System.Single

FieldNames

Gets or Sets 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. Set this to null for the field names to be determined at runtime from the IndexReader provided in the constructor.

Declaration

public string[] FieldNames { get; set; }

Property Value

Type	Description
System.String[]

MaxDocFreq

Gets or Sets 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 DEFAULT_MAX_DOC_FREQ.

Declaration

public int MaxDocFreq { get; set; }

Property Value

Type	Description
System.Int32

MaxNumTokensParsed

Declaration

public int MaxNumTokensParsed { get; set; }

Property Value

Type	Description
System.Int32	Gets or Sets 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

MaxQueryTerms

Gets or Sets the maximum number of query terms that will be included in any generated query. The default is DEFAULT_MAX_QUERY_TERMS.

Declaration

public int MaxQueryTerms { get; set; }

Property Value

Type	Description
System.Int32

MaxWordLen

Gets or Sets 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.

Declaration

public int MaxWordLen { get; set; }

Property Value

Type	Description
System.Int32

MinDocFreq

Gets or Sets 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.

Declaration

public int MinDocFreq { get; set; }

Property Value

Type	Description
System.Int32

MinTermFreq

Gets or Sets the frequency below which terms will be ignored in the source doc. The default frequency is the DEFAULT_MIN_TERM_FREQ.

Declaration

public int MinTermFreq { get; set; }

Property Value

Type	Description
System.Int32

MinWordLen

Gets or Sets 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.

Declaration

public int MinWordLen { get; set; }

Property Value

Type	Description
System.Int32

Similarity

For idf() calculations.

Declaration

public TFIDFSimilarity Similarity { get; set; }

Property Value

Type	Description
TFIDFSimilarity

StopWords

Gets or Sets 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".

Declaration

public ISet<string> StopWords { get; set; }

Property Value

Type	Description
ISet<System.String>

Methods

DescribeParams()

Describe the parameters that control how the "more like this" query is formed.

Declaration

public string DescribeParams()

Returns

Type	Description
System.String

Like(Int32)

Return a query that will return docs like the passed lucene document ID.

Declaration

public Query Like(int docNum)

Parameters

Type	Name	Description
System.Int32	docNum	the documentID of the lucene doc to generate the 'More Like This" query for.

Returns

Type	Description
Query	a query that will return docs like the passed lucene document ID.

Like(TextReader, String)

Return a query that will return docs like the passed .

Declaration

public Query Like(TextReader r, string fieldName)

Parameters

Type	Name	Description
TextReader	r
System.String	fieldName

Returns

Type	Description
Query	a query that will return docs like the passed .

RetrieveInterestingTerms(Int32)

Declaration

public string[] RetrieveInterestingTerms(int docNum)

Parameters

Type	Name	Description
System.Int32	docNum

Returns

Type	Description
System.String[]

See Also

RetrieveInterestingTerms(TextReader, String)

RetrieveInterestingTerms(TextReader, String)

Convenience routine to make it easy to return the most interesting words in a document. More advanced users will call RetrieveTerms(TextReader, String) directly.

Declaration

public string[] RetrieveInterestingTerms(TextReader r, string fieldName)

Parameters

Type	Name	Description
TextReader	r	the source document
System.String	fieldName	field passed to analyzer to use when analyzing the content

Returns

Type	Description
System.String[]	the most interesting words in the document

See Also

RetrieveTerms(TextReader, String)

MaxQueryTerms

RetrieveTerms(Int32)

Find words for a more-like-this query former.

Declaration

public PriorityQueue<object[]> RetrieveTerms(int docNum)

Parameters

Type	Name	Description
System.Int32	docNum	the id of the lucene document from which to find terms

Returns

Type	Description
PriorityQueue<System.Object[]>

RetrieveTerms(TextReader, String)

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:

• The word ()
• The top field that this word comes from ()
• The score for this word ()
• The IDF value ()
• The frequency of this word in the index ()
• The frequency of this word in the source document ()

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(TextReader, String).

Declaration

public PriorityQueue<object[]> RetrieveTerms(TextReader r, string fieldName)

Parameters

Type	Name	Description
TextReader	r	the reader that has the content of the document
System.String	fieldName	field passed to the analyzer to use when analyzing the content

Returns

Type	Description
PriorityQueue<System.Object[]>	the most interesting words in the document ordered by score, with the highest scoring, or best entry, first

See Also

RetrieveInterestingTerms(TextReader, String)

SetMaxDocFreqPct(Int32)

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.

Declaration

public void SetMaxDocFreqPct(int maxPercentage)

Parameters

Type	Name	Description
System.Int32	maxPercentage	the maximum percentage of documents (0-100) that a term may appear in to be still considered relevant