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## Package org.apache.lucene.search.vectorhighlight

This is an another highlighter implementation.

See:
Description

Interface Summary
BoundaryScanner |
FragListBuilder | FragListBuilder is an interface for FieldFragList builder classes.
FragmentsBuilder | `FragmentsBuilder` is an interface for fragments (snippets) builder classes.

Class Summary
BaseFragmentsBuilder |
BreakIteratorBoundaryScanner | A `BoundaryScanner` implementation that uses `BreakIterator` to find boundaries in the text.
FastVectorHighlighter | Another highlighter implementation.
FieldFragList | FieldFragList has a list of "frag info" that is used by FragmentsBuilder class to create fragments (snippets).
FieldFragList.WeightedFragInfo |
FieldFragList.WeightedFragInfo.SubInfo |
FieldPhraseList | FieldPhraseList has a list of WeightedPhraseInfo that is used by FragListBuilder to create a FieldFragList object.
FieldPhraseList.WeightedPhraseInfo |
FieldPhraseList.WeightedPhraseInfo.Toffs |
FieldQuery | FieldQuery breaks down query object into terms/phrases and keep them in QueryPhraseMap structure.
FieldQuery.QueryPhraseMap |
FieldTermStack | `FieldTermStack` is a stack that keeps query terms in the specified field of the document to be highlighted.
FieldTermStack.TermInfo |
ScoreOrderFragmentsBuilder | An implementation of FragmentsBuilder that outputs score-order fragments.
ScoreOrderFragmentsBuilder.ScoreComparator |
SimpleBoundaryScanner |
SimpleFragListBuilder | A simple implementation of `FragListBuilder`.
SimpleFragmentsBuilder | A simple implementation of FragmentsBuilder.
SingleFragListBuilder | An implementation class of `FragListBuilder` that generates one `FieldFragList.WeightedFragInfo` object.

## Package org.apache.lucene.search.vectorhighlight Description

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 TermVector.WITH_POSITIONS_OFFSETS
- take into account query boost to score fragments
- support colored highlight tags
- pluggable FragListBuilder
- 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 `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 `FieldTermStack`. Fast Vector Highlighter uses `TermFreqVector` data (must be stored `Field.TermVector.WITH_POSITIONS_OFFSETS`) 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 `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. The weight (Fast Vector Highlighter uses query boost to calculate the weight) will be taken into account when Fast Vector Highlighter creates `FieldFragList` in the next step.

### 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                     |
    +---------------------------------+

### Step 5.

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

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