org.apache.lucene.search.vectorhighlight.package.html Maven / Gradle / Ivy
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This is an another highlighter implementation.
Features
- fast for large docs
- support N-gram fields
- support phrase-unit highlighting with slops
- support multi-term (includes wildcard, range, regexp, etc) queries
- need Java 1.5
- highlight fields need to be stored with Positions and Offsets
- take into account query boost and/or IDF-weight to score fragments
- support colored highlight tags
- pluggable FragListBuilder / FieldFragList
- pluggable FragmentsBuilder
Algorithm
To explain the algorithm, let's use the following sample text
(to be highlighted) and user query:
Sample Text
Lucene is a search engine library.
User Query
Lucene^2 OR "search library"~1
The user query is a BooleanQuery that consists of TermQuery("Lucene")
with boost of 2 and PhraseQuery("search library") with slop of 1.
For your convenience, here is the offsets and positions info of the
sample text.
+--------+-----------------------------------+
| | 1111111111222222222233333|
| offset|01234567890123456789012345678901234|
+--------+-----------------------------------+
|document|Lucene is a search engine library. |
+--------*-----------------------------------+
|position|0 1 2 3 4 5 |
+--------*-----------------------------------+
Step 1.
In Step 1, Fast Vector Highlighter generates {@link org.apache.lucene.search.vectorhighlight.FieldQuery.QueryPhraseMap} from the user query.
QueryPhraseMap consists of the following members:
public class QueryPhraseMap {
boolean terminal;
int slop; // valid if terminal == true and phraseHighlight == true
float boost; // valid if terminal == true
Map<String, QueryPhraseMap> subMap;
}
QueryPhraseMap has subMap. The key of the subMap is a term
text in the user query and the value is a subsequent QueryPhraseMap.
If the query is a term (not phrase), then the subsequent QueryPhraseMap
is marked as terminal. If the query is a phrase, then the subsequent QueryPhraseMap
is not a terminal and it has the next term text in the phrase.
From the sample user query, the following QueryPhraseMap
will be generated:
QueryPhraseMap
+--------+-+ +-------+-+
|"Lucene"|o+->|boost=2|*| * : terminal
+--------+-+ +-------+-+
+--------+-+ +---------+-+ +-------+------+-+
|"search"|o+->|"library"|o+->|boost=1|slop=1|*|
+--------+-+ +---------+-+ +-------+------+-+
Step 2.
In Step 2, Fast Vector Highlighter generates {@link org.apache.lucene.search.vectorhighlight.FieldTermStack}. Fast Vector Highlighter uses term vector data
(must be stored {@link org.apache.lucene.document.FieldType#setStoreTermVectorOffsets(boolean)} and {@link org.apache.lucene.document.FieldType#setStoreTermVectorPositions(boolean)})
to generate it. FieldTermStack keeps the terms in the user query.
Therefore, in this sample case, Fast Vector Highlighter generates the following FieldTermStack:
FieldTermStack
+------------------+
|"Lucene"(0,6,0) |
+------------------+
|"search"(12,18,3) |
+------------------+
|"library"(26,33,5)|
+------------------+
where : "termText"(startOffset,endOffset,position)
Step 3.
In Step 3, Fast Vector Highlighter generates {@link org.apache.lucene.search.vectorhighlight.FieldPhraseList}
by reference to QueryPhraseMap and FieldTermStack.
FieldPhraseList
+----------------+-----------------+---+
|"Lucene" |[(0,6)] |w=2|
+----------------+-----------------+---+
|"search library"|[(12,18),(26,33)]|w=1|
+----------------+-----------------+---+
The type of each entry is WeightedPhraseInfo that consists of
an array of terms offsets and weight.
Step 4.
In Step 4, Fast Vector Highlighter creates FieldFragList by reference to
FieldPhraseList. In this sample case, the following
FieldFragList will be generated:
FieldFragList
+---------------------------------+
|"Lucene"[(0,6)] |
|"search library"[(12,18),(26,33)]|
|totalBoost=3 |
+---------------------------------+
The calculation for each FieldFragList.WeightedFragInfo.totalBoost (weight)
depends on the implementation of FieldFragList.add( ... ):
public void add( int startOffset, int endOffset, List<WeightedPhraseInfo> phraseInfoList ) {
float totalBoost = 0;
List<SubInfo> subInfos = new ArrayList<SubInfo>();
for( WeightedPhraseInfo phraseInfo : phraseInfoList ){
subInfos.add( new SubInfo( phraseInfo.getText(), phraseInfo.getTermsOffsets(), phraseInfo.getSeqnum() ) );
totalBoost += phraseInfo.getBoost();
}
getFragInfos().add( new WeightedFragInfo( startOffset, endOffset, subInfos, totalBoost ) );
}
The used implementation of FieldFragList is noted in BaseFragListBuilder.createFieldFragList( ... ):
public FieldFragList createFieldFragList( FieldPhraseList fieldPhraseList, int fragCharSize ){
return createFieldFragList( fieldPhraseList, new SimpleFieldFragList( fragCharSize ), fragCharSize );
}
Currently there are basically to approaches available:
SimpleFragListBuilder using SimpleFieldFragList: sum-of-boosts-approach. The totalBoost is calculated by summarizing the query-boosts per term. Per default a term is boosted by 1.0WeightedFragListBuilder using WeightedFieldFragList: sum-of-distinct-weights-approach. The totalBoost is calculated by summarizing the IDF-weights of distinct terms.
Comparison of the two approaches:
query = das alte testament (The Old Testament)
Terms in fragment sum-of-distinct-weights sum-of-boosts das alte testament 5.339621 3.0 das alte testament 5.339621 3.0 das testament alte 5.339621 3.0 das alte testament 5.339621 3.0 das testament 2.9455688 2.0 das alte 2.4759595 2.0 das das das das 1.5015357 4.0 das das das 1.3003681 3.0 das das 1.061746 2.0 alte 1.0 1.0 alte 1.0 1.0 das 0.7507678 1.0 das 0.7507678 1.0 das 0.7507678 1.0 das 0.7507678 1.0 das 0.7507678 1.0
Step 5.
In Step 5, by using FieldFragList and the field stored data,
Fast Vector Highlighter creates highlighted snippets!