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Lucene 925: analysis javadocs 

git-svn-id: https://svn.apache.org/repos/asf/lucene/java/trunk@547226 13f79535-47bb-0310-9956-ffa450edef68
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Grant Ingersoll 2007-06-14 12:09:02 +00:00
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@ -276,6 +276,8 @@ Documentation
4. LUCENE-740: Added SNOWBALL-LICENSE.txt to the snowball package and a 4. LUCENE-740: Added SNOWBALL-LICENSE.txt to the snowball package and a
remark about the license to NOTICE.TXT. (Steven Parkes via Michael Busch) remark about the license to NOTICE.TXT. (Steven Parkes via Michael Busch)
5. LUCENE-925: Added analysis package javadocs. (Grant Ingersoll and Doron Cohen)
Build Build
1. LUCENE-802: Added LICENSE.TXT and NOTICE.TXT to Lucene jars. 1. LUCENE-802: Added LICENSE.TXT and NOTICE.TXT to Lucene jars.

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src/java/org/apache/lucene/analysis/package.html
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<meta name="Author" content="Doug Cutting"> <meta name="Author" content="Doug Cutting">
</head> </head>
<body> <body>
API and code to convert text into indexable tokens. <p>API and code to convert text into indexable/searchable tokens. Covers {@link org.apache.lucene.analysis.Analyzer} and related classes.</p>
<h2>Parsing? Tokenization? Analysis!</h2>
<p>
Lucene, indexing and search library, accepts only plain text input.
<p>
<h2>Parsing</h2>
<p>
Applications that build their search capabilities upon Lucene may support documents in various formats - HTML, XML, PDF, Word - just to name a few.
Lucene does not care about the <i>Parsing</i> of these and other document formats, and it is the responsibility of the
application using Lucene to use an appropriate <i>Parser</i> to convert the original format into plain text, before passing that plain text to Lucene.
<p>
<h2>Tokenization</h2>
<p>
Plain text passed to Lucene for indexing goes through a process generally called tokenization - namely breaking of the
input text into small indexing elements - <i>Tokens</i>. The way that the input text is broken into tokens very
much dictates the further search capabilities of the index into which that text was added. Sentences
beginnings and endings can be identified to provide for more accurate phrase and proximity searches
(though sentence identification is not provided by Lucene).
<p>
In some cases simply breaking the input text into tokens is not enough - a deeper <i>Analysis</i> is needed,
providing for several functions, including (but not limited to):
<ul>
<li>Stemming -- Replacing of words by their stems. For instance with English stemming "bikes" is replaced by "bike"; now query "bike" can find both documents containing "bike"
and those containing "bikes". See <a href="http://en.wikipedia.org/wiki/Stemming">Wikipedia</a> for more information.</li>
<li>Stop words removal -- Common words like "the", "and" and "a" rarely add any value to a search. Removing them shrinks the index size and increases performance.</li>
<li>Character normalization -- Stripping accents and other character markings can make for better searching.</li>
<li>Synonyms expansion -- Adding in synonyms at the same token position as the current word can mean better matching when a users search with words in the synonym set.</li>
</ul>
<p>
<h2>Core Analysis</h2>
<p>
The analysis package provides the mechanism to convert Strings and Readers into tokens that can be indexed by Lucene. There
are three main classes in the package from which all analysis processes are derived. These are:
<ul>
<li>{@link org.apache.lucene.analysis.Analyzer} -- An Analyzer is responsible for building a TokenStream which can be consumed
by the indexing and searching processes. See below for more information on implementing your own Analyzer.</li>
<li>{@link org.apache.lucene.analysis.Tokenizer} -- A Tokenizer is a {@link org.apache.lucene.analysis.TokenStream} and is responsible for breaking
up incoming text into {@link org.apache.lucene.analysis.Token}s. In most cases, an Analyzer will use a Tokenizer as the first step in
the analysis process.</li>
<li>{@link org.apache.lucene.analysis.TokenFilter} -- A TokenFilter is also a {@link org.apache.lucene.analysis.TokenStream} and is responsible
for modifying {@link org.apache.lucene.analysis.Token}s that have been created by the Tokenizer. Common modifications performed by a
TokenFilter are: deletion, stemming, synonym injection, and down casing. Not all Analyzers require TokenFilters</li>
</ul>
</p>
<h2>Hints, Tips and Traps</h2>
<p>
The synergy between {@link org.apache.lucene.analysis.Analyzer} and {@link org.apache.lucene.analysis.Tokenizer}
is sometimes confusing. To ease on this confusion, some clarifications:
<ul>
<li>The {@link org.apache.lucene.analysis.Analyzer} is responsible for the entire task of
<u>creating</u> tokens out of the input text, while the {@link org.apache.lucene.analysis.Tokenizer}
is only responsible for <u>breaking</u> the input text into tokens. Very likely, tokens created
by the {@link org.apache.lucene.analysis.Tokenizer} would be modified or even omitted
by the {@link org.apache.lucene.analysis.Analyzer} before being returned.
</li>
<li>{@link org.apache.lucene.analysis.Tokenizer} is a {@link org.apache.lucene.analysis.TokenStream},
but {@link org.apache.lucene.analysis.Analyzer} is not.
</li>
<li>{@link org.apache.lucene.analysis.Analyzer} is "field aware", but
{@link org.apache.lucene.analysis.Tokenizer} is not.
</li>
</ul>
</p>
<p>Lucene Java provides a number of analysis capabilities, the most commonly used one being the {@link
org.apache.lucene.analysis.standard.StandardAnalyzer}. Many applications will have a long and industrious life with nothing more
than the StandardAnalyzer. However, there are a few other classes/packages that are worth mentioning:
<ol>
<li>{@link org.apache.lucene.analysis.PerFieldAnalyzerWrapper} -- Most Analyzers perform the same operation on all
{@link org.apache.lucene.document.Field}s. The PerFieldAnalyzerWrapper can be used to associate a different Analyzer with different
{@link org.apache.lucene.document.Field}s.</li>
<li>The contrib/analyzers library located at the root of the Lucene distribution has a number of different Analyzer implementations to solve a variety
of different problems related to searching. Many of the Analyzers are designed to analyze non-English languages.</li>
<li>The contrib/snowball library located at the root of the Lucene distribution has Analyzer and TokenFilter implementations for a variety of Snowball stemmers. See <a href="http://snowball.tartarus.org">http://snowball.tartarus.org</a> for more information.</li>
<li>There are a variety of Tokenizer and TokenFilter implementations in this package. Take a look around, chances are someone has implemented what you need.</li>
</ol>
</p>
<p>Analysis is one of the main causes of performance degradation during indexing. Simply put, the more you analyze the slower the indexing (in most cases).
Perhaps your application would be just fine using the simple {@link org.apache.lucene.analysis.WhitespaceTokenizer} combined with a
{@link org.apache.lucene.analysis.StopFilter}.</p>
<h2>Implementing your own Analyzer</h2>
<p>Creating your own Analyzer is straightforward. It usually involves either wrapping an existing Tokenizer and set of TokenFilters to create a new Analyzer
or creating both the Analyzer and a Tokenizer or TokenFilter. Before pursuing this approach, you may find it worthwhile
to explore the contrib/analyzers library and/or ask on the java-user@lucene.apache.org mailing list first to see if what you need already exists.
If you are still committed to creating your own Analyzer or TokenStream derivation (Tokenizer or TokenFilter) have a look at
the source code of any one of the many samples located in this package.</p>
</body> </body>
</html> </html>