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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:

<table border="1">
<tr>
<td>**Sample Text**</td>
<td>Lucene is a search engine library.</td>
</tr>
<tr>
<td>**User Query**</td>
<td>Lucene^2 OR "search library"~1</td>
</tr>
</table>

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 <xref:Lucene.Net.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 <xref:Lucene.Net.Search.VectorHighlight.FieldTermStack>. Fast Vector Highlighter uses term vector data (must be stored [#setStoreTermVectorOffsets(boolean)](xref:Lucene.Net.Documents.FieldType) and [#setStoreTermVectorPositions(boolean)](xref:Lucene.Net.Documents.FieldType)) 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 <xref:Lucene.Net.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 1.0
*   `WeightedFragListBuilder using WeightedFieldFragList`: *sum-of-distinct-weights*-approach. The totalBoost is calculated by summarizing the IDF-weights of distinct terms. 

Comparison of the two approaches:

<table border="1">
<caption>
	query = das alte testament (The Old Testament)
</caption>
<tr><th>Terms in fragment</th><th>sum-of-distinct-weights</th><th>sum-of-boosts</th></tr>
<tr><td>das alte testament</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das alte testament</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das testament alte</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das alte testament</td><td>5.339621</td><td>3.0</td></tr>
<tr><td>das testament</td><td>2.9455688</td><td>2.0</td></tr>
<tr><td>das alte</td><td>2.4759595</td><td>2.0</td></tr>
<tr><td>das das das das</td><td>1.5015357</td><td>4.0</td></tr>
<tr><td>das das das</td><td>1.3003681</td><td>3.0</td></tr>
<tr><td>das das</td><td>1.061746</td><td>2.0</td></tr>
<tr><td>alte</td><td>1.0</td><td>1.0</td></tr>
<tr><td>alte</td><td>1.0</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
<tr><td>das</td><td>0.7507678</td><td>1.0</td></tr>
</table>

### Step 5.

In Step 5, by using `FieldFragList` and the field stored data, Fast Vector Highlighter creates highlighted snippets!