cene.lucene-analyzers-icu.8.2.0.source-code.overview.html Maven / Gradle / Ivy
Show all versions of lucene-analyzers-icu Show documentation
Apache Lucene ICU integration module
This module exposes functionality from
ICU to Apache Lucene. ICU4J is a Java
library that enhances Java's internationalization support by improving
performance, keeping current with the Unicode Standard, and providing richer
APIs.
For an introduction to Lucene's analysis API, see the {@link org.apache.lucene.analysis} package documentation.
This module exposes the following functionality:
- Text Segmentation: Tokenizes text based on
properties and rules defined in Unicode.
- Collation: Compare strings according to the
conventions and standards of a particular language, region or country.
- Normalization: Converts text to a unique,
equivalent form.
- Case Folding: Removes case distinctions with
Unicode's Default Caseless Matching algorithm.
- Search Term Folding: Removes distinctions
(such as accent marks) between similar characters for a loose or fuzzy search.
- Text Transformation: Transforms Unicode text in
a context-sensitive fashion: e.g. mapping Traditional to Simplified Chinese
Text Segmentation
Text Segmentation (Tokenization) divides document and query text into index terms
(typically words). Unicode provides special properties and rules so that this can
be done in a manner that works well with most languages.
Text Segmentation implements the word segmentation specified in
Unicode Text Segmentation.
Additionally the algorithm can be tailored based on writing system, for example
text in the Thai script is automatically delegated to a dictionary-based segmentation
algorithm.
Use Cases
-
As a more thorough replacement for StandardTokenizer that works well for
most languages.
Example Usages
Tokenizing multilanguage text
/**
* This tokenizer will work well in general for most languages.
*/
Tokenizer tokenizer = new ICUTokenizer(reader);
Collation
ICUCollationKeyAnalyzer
converts each token into its binary CollationKey using the
provided Collator, allowing it to be
stored as an index term.
ICUCollationKeyAnalyzer depends on ICU4J to produce the
CollationKeys.
Use Cases
-
Efficient sorting of terms in languages that use non-Unicode character
orderings. (Lucene Sort using a Locale can be very slow.)
-
Efficient range queries over fields that contain terms in languages that
use non-Unicode character orderings. (Range queries using a Locale can be
very slow.)
-
Effective Locale-specific normalization (case differences, diacritics, etc.).
({@link org.apache.lucene.analysis.LowerCaseFilter} and
{@link org.apache.lucene.analysis.miscellaneous.ASCIIFoldingFilter} provide these services
in a generic way that doesn't take into account locale-specific needs.)
Example Usages
Farsi Range Queries
Collator collator = Collator.getInstance(new ULocale("ar"));
ICUCollationKeyAnalyzer analyzer = new ICUCollationKeyAnalyzer(collator);
Path indexPath = Files.createTempDirectory("tempIndex");
Directory dir = FSDirectory.open(indexPath);
IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(analyzer));
Document doc = new Document();
doc.add(new Field("content", "\u0633\u0627\u0628",
Field.Store.YES, Field.Index.ANALYZED));
writer.addDocument(doc);
writer.close();
IndexSearcher is = new IndexSearcher(dir, true);
QueryParser aqp = new QueryParser("content", analyzer);
aqp.setAnalyzeRangeTerms(true);
// Unicode order would include U+0633 in [ U+062F - U+0698 ], but Farsi
// orders the U+0698 character before the U+0633 character, so the single
// indexed Term above should NOT be returned by a ConstantScoreRangeQuery
// with a Farsi Collator (or an Arabic one for the case when Farsi is not
// supported).
ScoreDoc[] result
= is.search(aqp.parse("[ \u062F TO \u0698 ]"), null, 1000).scoreDocs;
assertEquals("The index Term should not be included.", 0, result.length);
Danish Sorting
Analyzer analyzer
= new ICUCollationKeyAnalyzer(Collator.getInstance(new ULocale("da", "dk")));
Path indexPath = Files.createTempDirectory("tempIndex");
Directory dir = FSDirectory.open(indexPath);
IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(analyzer));
String[] tracer = new String[] { "A", "B", "C", "D", "E" };
String[] data = new String[] { "HAT", "HUT", "H\u00C5T", "H\u00D8T", "HOT" };
String[] sortedTracerOrder = new String[] { "A", "E", "B", "D", "C" };
for (int i = 0 ; i < data.length ; ++i) {
Document doc = new Document();
doc.add(new Field("tracer", tracer[i], Field.Store.YES, Field.Index.NO));
doc.add(new Field("contents", data[i], Field.Store.NO, Field.Index.ANALYZED));
writer.addDocument(doc);
}
writer.close();
IndexSearcher searcher = new IndexSearcher(dir, true);
Sort sort = new Sort();
sort.setSort(new SortField("contents", SortField.STRING));
Query query = new MatchAllDocsQuery();
ScoreDoc[] result = searcher.search(query, null, 1000, sort).scoreDocs;
for (int i = 0 ; i < result.length ; ++i) {
Document doc = searcher.doc(result[i].doc);
assertEquals(sortedTracerOrder[i], doc.getValues("tracer")[0]);
}
Turkish Case Normalization
Collator collator = Collator.getInstance(new ULocale("tr", "TR"));
collator.setStrength(Collator.PRIMARY);
Analyzer analyzer = new ICUCollationKeyAnalyzer(collator);
Path indexPath = Files.createTempDirectory("tempIndex");
Directory dir = FSDirectory.open(indexPath);
IndexWriter writer = new IndexWriter(dir, new IndexWriterConfig(analyzer));
Document doc = new Document();
doc.add(new Field("contents", "DIGY", Field.Store.NO, Field.Index.ANALYZED));
writer.addDocument(doc);
writer.close();
IndexSearcher is = new IndexSearcher(dir, true);
QueryParser parser = new QueryParser("contents", analyzer);
Query query = parser.parse("d\u0131gy"); // U+0131: dotless i
ScoreDoc[] result = is.search(query, null, 1000).scoreDocs;
assertEquals("The index Term should be included.", 1, result.length);
Caveats and Comparisons
WARNING: Make sure you use exactly the same
Collator at index and query time -- CollationKeys
are only comparable when produced by
the same Collator. Since {@link java.text.RuleBasedCollator}s
are not independently versioned, it is unsafe to search against stored
CollationKeys unless the following are exactly the same (best
practice is to store this information with the index and check that they
remain the same at query time):
- JVM vendor
- JVM version, including patch version
-
The language (and country and variant, if specified) of the Locale
used when constructing the collator via
{@link java.text.Collator#getInstance(java.util.Locale)}.
-
The collation strength used - see {@link java.text.Collator#setStrength(int)}
ICUCollationKeyAnalyzer uses ICU4J's Collator, which
makes its version available, thus allowing collation to be versioned
independently from the JVM. ICUCollationKeyAnalyzer is also
significantly faster and generates significantly shorter keys than
CollationKeyAnalyzer. See
http://site.icu-project.org/charts/collation-icu4j-sun for key
generation timing and key length comparisons between ICU4J and
java.text.Collator over several languages.
CollationKeys generated by java.text.Collators are
not compatible with those those generated by ICU Collators. Specifically, if
you use CollationKeyAnalyzer to generate index terms, do not use
ICUCollationKeyAnalyzer on the query side, or vice versa.
Normalization
ICUNormalizer2Filter normalizes term text to a
Unicode Normalization Form, so
that equivalent
forms are standardized to a unique form.
Use Cases
- Removing differences in width for Asian-language text.
- Standardizing complex text with non-spacing marks so that characters are
ordered consistently.
Example Usages
Normalizing text to NFC
/**
* Normalizer2 objects are unmodifiable and immutable.
*/
Normalizer2 normalizer = Normalizer2.getInstance(null, "nfc", Normalizer2.Mode.COMPOSE);
/**
* This filter will normalize to NFC.
*/
TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, normalizer);
Case Folding
Default caseless matching, or case-folding is more than just conversion to
lowercase. For example, it handles cases such as the Greek sigma, so that
"Μάϊος" and "ΜΆΪΟΣ" will match correctly.
Case-folding is still only an approximation of the language-specific rules
governing case. If the specific language is known, consider using
ICUCollationKeyFilter and indexing collation keys instead. This implementation
performs the "full" case-folding specified in the Unicode standard, and this
may change the length of the term. For example, the German ß is case-folded
to the string 'ss'.
Case folding is related to normalization, and as such is coupled with it in
this integration. To perform case-folding, you use normalization with the form
"nfkc_cf" (which is the default).
Use Cases
-
As a more thorough replacement for LowerCaseFilter that has good behavior
for most languages.
Example Usages
Lowercasing text
/**
* This filter will case-fold and normalize to NFKC.
*/
TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer);
Search Term Folding
Search term folding removes distinctions (such as accent marks) between
similar characters. It is useful for a fuzzy or loose search.
Search term folding implements many of the foldings specified in
Character Foldings
as a special normalization form. This folding applies NFKC, Case Folding, and
many character foldings recursively.
Use Cases
-
As a more thorough replacement for ASCIIFoldingFilter and LowerCaseFilter
that applies the same ideas to many more languages.
Example Usages
Removing accents
/**
* This filter will case-fold, remove accents and other distinctions, and
* normalize to NFKC.
*/
TokenStream tokenstream = new ICUFoldingFilter(tokenizer);
Text Transformation
ICU provides text-transformation functionality via its Transliteration API. This allows
you to transform text in a variety of ways, taking context into account.
For more information, see the
User's Guide
and
Rule Tutorial.
Use Cases
-
Convert Traditional to Simplified
-
Transliterate between different writing systems: e.g. Romanization
Example Usages
Convert Traditional to Simplified
/**
* This filter will map Traditional Chinese to Simplified Chinese
*/
TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Traditional-Simplified"));
Transliterate Serbian Cyrillic to Serbian Latin
/**
* This filter will map Serbian Cyrillic to Serbian Latin according to BGN rules
*/
TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Serbian-Latin/BGN"));
Backwards Compatibility
This module exists to provide up-to-date Unicode functionality that supports
the most recent version of Unicode (currently 11.0). However, some users who wish
for stronger backwards compatibility can restrict
{@link org.apache.lucene.analysis.icu.ICUNormalizer2Filter} to operate on only
a specific Unicode Version by using a {@link com.ibm.icu.text.FilteredNormalizer2}.
Example Usages
Restricting normalization to Unicode 5.0
/**
* This filter will do NFC normalization, but will ignore any characters that
* did not exist as of Unicode 5.0. Because of the normalization stability policy
* of Unicode, this is an easy way to force normalization to a specific version.
*/
Normalizer2 normalizer = Normalizer2.getInstance(null, "nfc", Normalizer2.Mode.COMPOSE);
UnicodeSet set = new UnicodeSet("[:age=5.0:]");
// see FilteredNormalizer2 docs, the set should be frozen or performance will suffer
set.freeze();
FilteredNormalizer2 unicode50 = new FilteredNormalizer2(normalizer, set);
TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, unicode50);