Language Analysis

This section contains information about tokenizers and filters related to character set conversion or for use with specific languages.

For the European languages, tokenization is fairly straightforward. Tokens are delimited by white space and/or a relatively small set of punctuation characters.

In other languages the tokenization rules are often not so simple. Some European languages may also require special tokenization rules, such as rules for decompounding German words.

For information about language detection at index time, see Language Detection.

KeywordMarkerFilterFactory

Protects words from being modified by stemmers. A customized protected word list may be specified with the "protected" attribute in the schema. Any words in the protected word list will not be modified by any stemmer in Solr.

A sample Solr protwords.txt with comments can be found in the sample_techproducts_configs configset directory:

With name

<fieldtype name="myfieldtype" class="solr.TextField">
  <analyzer>
    <tokenizer name="whitespace"/>
    <filter name="keywordMarker" protected="protwords.txt" />
    <filter name="porterStem" />
  </analyzer>
</fieldtype>

With class name (legacy)

<fieldtype name="myfieldtype" class="solr.TextField">
  <analyzer>
    <tokenizer class="solr.WhitespaceTokenizerFactory"/>
    <filter class="solr.KeywordMarkerFilterFactory" protected="protwords.txt" />
    <filter class="solr.PorterStemFilterFactory" />
  </analyzer>
</fieldtype>

KeywordRepeatFilterFactory

Emits each token twice, one with the KEYWORD attribute and once without.

If placed before a stemmer, the result will be that you will get the unstemmed token preserved on the same position as the stemmed one. Queries matching the original exact term will get a better score while still maintaining the recall benefit of stemming. Another advantage of keeping the original token is that wildcard truncation will work as expected.

To configure, add the KeywordRepeatFilterFactory early in the analysis chain. It is recommended to also include RemoveDuplicatesTokenFilterFactory to avoid duplicates when tokens are not stemmed.

A sample fieldType configuration could look like this:

With name

<fieldtype name="english_stem_preserve_original" class="solr.TextField">
  <analyzer>
    <tokenizer name="standard"/>
    <filter name="keywordRepeat" />
    <filter name="porterStem" />
    <filter name="removeDuplicates" />
  </analyzer>
</fieldtype>

With class name (legacy)

<fieldtype name="english_stem_preserve_original" class="solr.TextField">
  <analyzer>
    <tokenizer class="solr.StandardTokenizerFactory"/>
    <filter class="solr.KeywordRepeatFilterFactory" />
    <filter class="solr.PorterStemFilterFactory" />
    <filter class="solr.RemoveDuplicatesTokenFilterFactory" />
  </analyzer>
</fieldtype>

When adding the same token twice, it will also score twice (double), so you may have to re-tune your ranking rules.

StemmerOverrideFilterFactory

Overrides stemming algorithms by applying a custom mapping, then protecting these terms from being modified by stemmers.

A customized mapping of words to stems, in a tab-separated file, can be specified to the dictionary attribute in the schema. Words in this mapping will be stemmed to the stems from the file, and will not be further changed by any stemmer.

With name

<fieldtype name="myfieldtype" class="solr.TextField">
  <analyzer>
    <tokenizer name="whitespace"/>
    <filter name="stemmerOverride" dictionary="stemdict.txt" />
    <filter name="porterStem" />
  </analyzer>
</fieldtype>

With class name (legacy)

<fieldtype name="myfieldtype" class="solr.TextField">
  <analyzer>
    <tokenizer class="solr.WhitespaceTokenizerFactory"/>
    <filter class="solr.StemmerOverrideFilterFactory" dictionary="stemdict.txt" />
    <filter class="solr.PorterStemFilterFactory" />
  </analyzer>
</fieldtype>

A sample stemdict.txt file is shown below:

# these must be tab-separated
monkeys	monkey
otters	otter
# some crazy ones that a stemmer would never do
dogs	cat

If you have a checkout of Solr’s source code locally, you can also find this example in Solr’s test resources at solr/core/src/test-files/solr/collection1/conf/stemdict.txt.

Dictionary Compound Word Token Filter

This filter splits, or decompounds, compound words into individual words using a dictionary of the component words. Each input token is passed through unchanged. If it can also be decompounded into subwords, each subword is also added to the stream at the same logical position.

Compound words are most commonly found in Germanic languages.

Factory class: solr.DictionaryCompoundWordTokenFilterFactory

Arguments:

dictionary

Required

Default: none

The path of a file that contains a list of simple words, one per line. Blank lines and lines that begin with “#” are ignored.

See Resource Loading for more information.

minWordSize

Optional

Default: 5

Any token shorter than this is not decompounded.

minSubwordSize

Optional

Default: 2

Subwords shorter than this are not emitted as tokens.

maxSubwordSize

Optional

Default: 15

Subwords longer than this are not emitted as tokens.

onlyLongestMatch

Optional

Default: true

If true, only the longest matching subwords will generate new tokens.

Example:

Assume that germanwords.txt contains at least the following words: dumm kopf donau dampf schiff

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="dictionaryCompoundWord" dictionary="germanwords.txt"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.DictionaryCompoundWordTokenFilterFactory" dictionary="germanwords.txt"/>
</analyzer>

In: "Donaudampfschiff dummkopf"

Tokenizer to Filter: "Donaudampfschiff"(1), "dummkopf"(2),

Out: "Donaudampfschiff"(1), "Donau"(1), "dampf"(1), "schiff"(1), "dummkopf"(2), "dumm"(2), "kopf"(2)

Unicode Collation

Unicode Collation is a language-sensitive method of sorting text that can also be used for advanced search purposes.

Unicode Collation in Solr is fast, because all the work is done at index time.

Rather than specifying an analyzer within <fieldtype … class="solr.TextField">, the solr.CollationField and solr.ICUCollationField field type classes provide this functionality. solr.ICUCollationField, which is backed by the ICU4J library, provides more flexible configuration, has more locales, is significantly faster, and requires less memory and less index space, since its keys are smaller than those produced by the JDK implementation that backs solr.CollationField.

To use solr.ICUCollationField, you must enable the analysis-extras Module.

solr.ICUCollationField and solr.CollationField fields can be created in two ways:

Based upon a system collator associated with a locale.
Based upon a tailored RuleBasedCollator ruleset.

ICUCollationField Attributes

Using a system collator

locale

Required

Default: none

RFC 3066 locale ID.

strength

Optional

Default: none

Valid values are primary, secondary, tertiary, quaternary, or identical. See Comparison Levels in ICU Collation Concepts for more information.

decomposition

Optional

Default: none

Valid values are no or canonical. See Normalization in ICU Collation Concepts for more information.

Using a Tailored ruleset

custom

Required

Default: none

Path to a UTF-8 text file containing rules supported by the ICU RuleBasedCollator

strength

Optional

Default: none

Valid values are primary, secondary, tertiary, quaternary, or identical. See Comparison Levels in ICU Collation Concepts for more information.

decomposition

Optional

Default: none

Valid values are no or canonical. See Normalization in ICU Collation Concepts for more information.

Expert options

alternate

Optional

Default: none

Valid values are shifted or non-ignorable. Can be used to ignore punctuation or whitespace.

caseLevel

Optional

Default: false

If true, in combination with strength="primary", accents are ignored but case is taken into account. See CaseLevel in ICU Collation Concepts for more information.

caseFirst

Optional

Default: none

Valid values are lower or upper. Useful to control which is sorted first when case is not ignored.

numeric

Optional

Default: false

If true, digits are sorted according to numeric value, e.g., foobar-9 sorts before foobar-10.

variableTop

Optional

Default: none

Single character or contraction. Controls what is variable for alternate.

Sorting Text for a Specific Language

In this example, text is sorted according to the default German rules provided by ICU4J.

Locales are typically defined as a combination of language and country, but you can specify just the language if you want. For example, if you specify "de" as the language, you will get sorting that works well for the German language. If you specify "de" as the language and "CH" as the country, you will get German sorting specifically tailored for Switzerland.

<!-- Define a field type for German collation -->
<fieldType name="collatedGERMAN" class="solr.ICUCollationField"
           locale="de"
           strength="primary" />
...
<!-- Define a field to store the German collated manufacturer names. -->
<field name="manuGERMAN" type="collatedGERMAN" indexed="false" stored="false" docValues="true"/>
...
<!-- Copy the text to this field. We could create French, English, Spanish versions too,
     and sort differently for different users! -->
<copyField source="manu" dest="manuGERMAN"/>

In the example above, we defined the strength as "primary". The strength of the collation determines how strict the sort order will be, but it also depends upon the language. For example, in English, "primary" strength ignores differences in case and accents.

Another example:

<fieldType name="polishCaseInsensitive" class="solr.ICUCollationField"
           locale="pl_PL"
           strength="secondary" />
...
<field name="city" type="text_general" indexed="true" stored="true"/>
...
<field name="city_sort" type="polishCaseInsensitive" indexed="true" stored="false"/>
...
<copyField source="city" dest="city_sort"/>

The type will be used for the fields where the data contains Polish text. The "secondary" strength will ignore case differences, but, unlike "primary" strength, a letter with diacritic(s) will be sorted differently from the same base letter without diacritics.

An example using the "city_sort" field to sort:

q=*:*&fl=city&sort=city_sort+asc

Sorting Text for Multiple Languages

There are two approaches to supporting multiple languages: if there is a small list of languages you wish to support, consider defining collated fields for each language and using copyField. However, adding a large number of sort fields can increase disk and indexing costs. An alternative approach is to use the Unicode default collator.

The Unicode default or ROOT locale has rules that are designed to work well for most languages. To use the default locale, simply define the locale as the empty string. This Unicode default sort is still significantly more advanced than the standard Solr sort.

<fieldType name="collatedROOT" class="solr.ICUCollationField"
           locale=""
           strength="primary" />

Sorting Text with Custom Rules

You can define your own set of sorting rules. It’s easiest to take existing rules that are close to what you want and customize them.

In the example below, we create a custom rule set for German called DIN 5007-2. This rule set treats umlauts in German differently: it treats ö as equivalent to oe, ä as equivalent to ae, and ü as equivalent to ue. For more information, see the ICU RuleBasedCollator javadocs.

This example shows how to create a custom rule set for solr.ICUCollationField and dump it to a file:

// get the default rules for Germany
// these are called DIN 5007-1 sorting
RuleBasedCollator baseCollator = (RuleBasedCollator) Collator.getInstance(new ULocale("de", "DE"));

// define some tailorings, to make it DIN 5007-2 sorting.
// For example, this makes ö equivalent to oe
String DIN5007_2_tailorings =
    "& ae , a\u0308 & AE , A\u0308"+
    "& oe , o\u0308 & OE , O\u0308"+
    "& ue , u\u0308 & UE , u\u0308";

// concatenate the default rules to the tailorings, and dump it to a String
RuleBasedCollator tailoredCollator = new RuleBasedCollator(baseCollator.getRules() + DIN5007_2_tailorings);
String tailoredRules = tailoredCollator.getRules();

// write these to a file, be sure to use UTF-8 encoding!!!
FileOutputStream os = new FileOutputStream(new File("/solr_home/conf/customRules.dat"));
IOUtils.write(tailoredRules, os, "UTF-8");

This rule set can now be used for custom collation in Solr:

<fieldType name="collatedCUSTOM" class="solr.ICUCollationField"
           custom="customRules.dat"
           strength="primary" />

JDK Collation

As mentioned above, ICU Unicode Collation is better in several ways than JDK Collation, but if you cannot use ICU4J for some reason, you can use solr.CollationField.

The principles of JDK Collation are the same as those of ICU Collation; you just specify language, country and variant arguments instead of the combined locale argument.

JDK Collation Attributes

Using a System collator (see Oracle’s list of locales supported in Java):

language

Required

Default: none

The ISO-639 language code.

country

Optional

Default: none

The ISO-3166 country code.

variant

Optional

Default: none

Vendor or browser-specific code.

strength

Optional

Default: none

Valid values are primary, secondary, tertiary or identical. See Java Collator javadocs for more information.

decomposition

Optional

Default: none

Valid values are no, canonical, or full. See Java Collator javadocs for more information.

Using a Tailored ruleset:

custom

Required

Default: none

Path to a UTF-8 text file containing rules supported by the JDK RuleBasedCollator.

strength

Optional

Default: none

Valid values are primary, secondary, tertiary or identical. See Java Collator javadocs for more information.

decomposition

Optional

Default: none

Valid values are no, canonical, or full. See Java Collator javadocs for more information.

A solr.CollationField example:

<fieldType name="collatedGERMAN" class="solr.CollationField"
           language="de"
           country="DE"
           strength="primary" /> <!-- ignore Umlauts and letter case when sorting -->
...
<field name="manuGERMAN" type="collatedGERMAN" indexed="false" stored="false" docValues="true" />
...
<copyField source="manu" dest="manuGERMAN"/>

ASCII & Decimal Folding Filters

ASCII Folding

This filter converts alphabetic, numeric, and symbolic Unicode characters which are not in the first 127 ASCII characters (the "Basic Latin" Unicode block) into their ASCII equivalents, if one exists. Only those characters with reasonable ASCII alternatives are converted.

This can increase recall by causing more matches. On the other hand, it can reduce precision because language-specific character differences may be lost.

Factory class: solr.ASCIIFoldingFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="asciiFolding"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.ASCIIFoldingFilterFactory"/>
</analyzer>

In: "Björn Ångström"

Tokenizer to Filter: "Björn", "Ångström"

Out: "Bjorn", "Angstrom"

Decimal Digit Folding

This filter converts any character in the Unicode "Decimal Number" general category (Nd) into their equivalent Basic Latin digits (0-9).

This can increase recall by causing more matches. On the other hand, it can reduce precision because language-specific character differences may be lost.

Factory class: solr.DecimalDigitFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="decimalDigit"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.DecimalDigitFilterFactory"/>
</analyzer>

OpenNLP Integration

The Lucene module lucene/analysis/opennlp provides OpenNLP integration via several analysis components: a tokenizer, a part-of-speech tagging filter, a phrase chunking filter, and a lemmatization filter. In addition to these analysis components, Solr also provides an update request processor to extract named entities. See also Update Processor Factories That Can Be Loaded as Plugins.

The OpenNLP Tokenizer must be used with all other OpenNLP analysis components, for two reasons. First, the OpenNLP Tokenizer detects and marks the sentence boundaries required by all the OpenNLP filters. Second, since the pre-trained OpenNLP models used by these filters were trained using the corresponding language-specific sentence-detection/tokenization models, the same tokenization using the same models must be used at runtime for optimal performance.

To use the OpenNLP components, you must enable the analysis-extras Module.

OpenNLP Tokenizer

The OpenNLP Tokenizer takes two language-specific binary model files as parameters: a sentence detector model and a tokenizer model. The last token in each sentence is flagged, so that following OpenNLP-based filters can use this information to apply operations to tokens one sentence at a time. See the OpenNLP website for information on downloading pre-trained models.

Factory class: solr.OpenNLPTokenizerFactory

Arguments:

sentenceModel

Required

Default: none

The path of a language-specific OpenNLP sentence detection model file. See Resource Loading for more information.

tokenizerModel

Required

Default: none

The path of a language-specific OpenNLP tokenization model file. See Resource Loading for more information.

Example:

With name

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.OpenNLPTokenizerFactory"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
</analyzer>

OpenNLP Part-Of-Speech Filter

This filter sets each token’s type attribute to the part of speech (POS) assigned by the configured model. See the OpenNLP website for information on downloading pre-trained models.

Lucene currently does not index token types, so if you want to keep this information, you have to preserve it either in a payload or as a synonym; see the examples below.

Factory class: solr.OpenNLPPOSFilterFactory

Arguments:

posTaggerModel

Required

Default: none

The path of a language-specific OpenNLP POS tagger model file. See Resource Loading for more information.

Examples:

The OpenNLP tokenizer will tokenize punctuation, which is useful for following token filters. Ordinarily you don’t want to include punctuation in your index, so the TypeTokenFilter is included in the examples below, with stop.pos.txt containing the following:

stop.pos.txt

#
$
''
``
,
-LRB-
-RRB-
:
.

Index the POS for each token as a payload:

With name

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="typeAsPayload"/>
  <filter name="type" types="stop.pos.txt"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.OpenNLPTokenizerFactory"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter class="solr.OpenNLPPOSFilterFactory" posTaggerModel="en-pos-maxent.bin"/>
  <filter class="solr.TypeAsPayloadFilterFactory"/>
  <filter class="solr.TypeTokenFilterFactory" types="stop.pos.txt"/>
</analyzer>

Index the POS for each token as a synonym, after prefixing the POS with "@" (see the TypeAsSynonymFilter description):

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="typeAsSynonym" prefix="@"/>
  <filter name="type" types="stop.pos.txt"/>
</analyzer>

Only index nouns - the keep.pos.txt file contains lines NN, NNS, NNP and NNPS:

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="type" types="keep.pos.txt" useWhitelist="true"/>
</analyzer>

OpenNLP Phrase Chunking Filter

This filter sets each token’s type attribute based on the output of an OpenNLP phrase chunking model. The chunk labels replace the POS tags that previously were in each token’s type attribute. See the OpenNLP website for information on downloading pre-trained models.

Prerequisite: the OpenNLP Tokenizer and the OpenNLP Part-Of-Speech Filter must precede this filter.

Lucene currently does not index token types, so if you want to keep this information, you have to preserve it either in a payload or as a synonym; see the examples below.

Factory class: solr.OpenNLPChunkerFilterFactory

Arguments:

chunkerModel

Required

Default: none

The path of a language-specific OpenNLP phrase chunker model file. See Resource Loading for more information.

Examples:

Index the phrase chunk label for each token as a payload:

With name

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="openNLPChunker" chunkerModel="en-chunker.bin"/>
  <filter name="typeAsPayload"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.OpenNLPTokenizerFactory"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter class="solr.OpenNLPPOSFilterFactory" posTaggerModel="en-pos-maxent.bin"/>
  <filter class="solr.OpenNLPChunkerFilterFactory" chunkerModel="en-chunker.bin"/>
  <filter class="solr.TypeAsPayloadFilterFactory"/>
</analyzer>

Index the phrase chunk label for each token as a synonym, after prefixing it with "#" (see the TypeAsSynonymFilter description):

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="openNLPChunker" chunkerModel="en-chunker.bin"/>
  <filter name="typeAsSynonym" prefix="#"/>
</analyzer>

OpenNLP Lemmatizer Filter

This filter replaces the text of each token with its lemma. Both a dictionary-based lemmatizer and a model-based lemmatizer are supported. If both are configured, the dictionary-based lemmatizer is tried first, and then the model-based lemmatizer is consulted for out-of-vocabulary tokens. See the OpenNLP website for information on downloading pre-trained models.

Factory class: solr.OpenNLPLemmatizerFilter

Arguments:

Either dictionary or lemmatizerModel must be provided, and both may be provided - see the examples below:

dictionary

Optional

Default: none

The path of a lemmatization dictionary file. See Resource Loading for more information. The dictionary file must be encoded as UTF-8, with one entry per line, in the form word[tab]lemma[tab]part-of-speech, e.g., wrote[tab]write[tab]VBD.

lemmatizerModel

Optional

Default: none

The path of a language-specific OpenNLP lemmatizer model file. See Resource Loading for more information.

Examples:

Perform dictionary-based lemmatization, and fall back to model-based lemmatization for out-of-vocabulary tokens (see the OpenNLP Part-Of-Speech Filter section above for information about using TypeTokenFilter to avoid indexing punctuation):

With name

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="oenNLPLemmatizer"
          dictionary="lemmas.txt"
          lemmatizerModel="en-lemmatizer.bin"/>
  <filter name="type" types="stop.pos.txt"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.OpenNLPTokenizerFactory"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter class="solr.OpenNLPPOSFilterFactory" posTaggerModel="en-pos-maxent.bin"/>
  <filter class="solr.OpenNLPLemmatizerFilterFactory"
          dictionary="lemmas.txt"
          lemmatizerModel="en-lemmatizer.bin"/>
  <filter class="solr.TypeTokenFilterFactory" types="stop.pos.txt"/>
</analyzer>

Perform dictionary-based lemmatization only:

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="openNLPLemmatizer" dictionary="lemmas.txt"/>
  <filter name="type" types="stop.pos.txt"/>
</analyzer>

Perform model-based lemmatization only, preserving the original token and emitting the lemma as a synonym (see the KeywordRepeatFilterFactory description)):

<analyzer>
  <tokenizer name="openNLP"
             sentenceModel="en-sent.bin"
             tokenizerModel="en-tokenizer.bin"/>
  <filter name="openNLPPOS" posTaggerModel="en-pos-maxent.bin"/>
  <filter name="keywordRepeat"/>
  <filter name="openNLPLemmatizer" lemmatizerModel="en-lemmatizer.bin"/>
  <filter name="removeDuplicates"/>
  <filter name="type" types="stop.pos.txt"/>
</analyzer>

Language-Specific Factories

These factories are each designed to work with specific languages. The languages covered here are:

Arabic

Solr provides support for the Light-10 (PDF) stemming algorithm, and Lucene includes an example stopword list.

This algorithm defines both character normalization and stemming, so these are split into two filters to provide more flexibility.

Factory classes: solr.ArabicStemFilterFactory, solr.ArabicNormalizationFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="arabicNormalization"/>
  <filter name="arabicStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.ArabicNormalizationFilterFactory"/>
  <filter class="solr.ArabicStemFilterFactory"/>
</analyzer>

Bengali

There are two filters written specifically for dealing with Bengali language. They use the Lucene classes org.apache.lucene.analysis.bn.BengaliNormalizationFilter and org.apache.lucene.analysis.bn.BengaliStemFilter.

Factory classes: solr.BengaliStemFilterFactory, solr.BengaliNormalizationFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="bengaliNormalization"/>
  <filter name="bengaliStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.BengaliNormalizationFilterFactory"/>
  <filter class="solr.BengaliStemFilterFactory"/>
</analyzer>

Normalisation - মানুষ → মানুস

Stemming - সমস্ত → সমস্

Brazilian Portuguese

This is a Java filter written specifically for stemming the Brazilian dialect of the Portuguese language. It uses the Lucene class org.apache.lucene.analysis.br.BrazilianStemmer. Although that stemmer can be configured to use a list of protected words (which should not be stemmed), this factory does not accept any arguments to specify such a list.

Factory class: solr.BrazilianStemFilterFactory

Arguments: None

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="brazilianStem"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.BrazilianStemFilterFactory"/>
</analyzer>

In: "praia praias"

Tokenizer to Filter: "praia", "praias"

Out: "pra", "pra"

Bulgarian

Solr includes a light stemmer for Bulgarian, following this algorithm (PDF), and Lucene includes an example stopword list.

Factory class: solr.BulgarianStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="bulgarianStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.BulgarianStemFilterFactory"/>
</analyzer>

Catalan

Solr can stem Catalan using the Snowball Porter Stemmer with an argument of language="Catalan". Solr includes a set of contractions for Catalan, which can be stripped using solr.ElisionFilterFactory.

Factory class: solr.SnowballPorterFilterFactory

Arguments:

language:

Required

Default: none

+ The stemmer language, Catalan in this case.

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="elision"
          articles="lang/contractions_ca.txt"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Catalan" />
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.ElisionFilterFactory"
          articles="lang/contractions_ca.txt"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Catalan" />
</analyzer>

In: "llengües llengua"

Tokenizer to Filter: "llengües"(1) "llengua"(2),

Out: "llengu"(1), "llengu"(2)

Traditional Chinese

The default configuration of the ICU Tokenizer is suitable for Traditional Chinese text. It follows the Word Break rules from the Unicode Text Segmentation algorithm for non-Chinese text, and uses a dictionary to segment Chinese words.

To use this tokenizer, you must enable the analysis-extras Module.

The Standard Tokenizer can also be used to tokenize Traditional Chinese text. Following the Word Break rules from the Unicode Text Segmentation algorithm, it produces one token per Chinese character. When combined with CJK Bigram Filter, overlapping bigrams of Chinese characters are formed.

CJK Width Filter folds fullwidth ASCII variants into the equivalent Basic Latin forms.

Examples:

With name

<analyzer>
  <tokenizer name="icu"/>
  <filter name="cjkWidth"/>
  <filter name="lowercase"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.ICUTokenizerFactory"/>
  <filter class="solr.CJKWidthFilterFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
</analyzer>

<analyzer>
  <tokenizer name="standard"/>
  <filter name="cjkBigram"/>
  <filter name="cjkWidth"/>
  <filter name="lowercase"/>
</analyzer>

CJK Bigram Filter

Forms bigrams (overlapping 2-character sequences) of CJK characters that are generated from the Standard Tokenizer or the ICU Tokenizer.

By default, all CJK characters produce bigrams, but finer grained control is available by specifying orthographic type arguments han, hiragana, katakana, and hangul. When set to false, characters of the corresponding type will be passed through as unigrams, and will not be included in any bigrams.

When a CJK character has no adjacent characters to form a bigram, it is output in unigram form. If you want to always output both unigrams and bigrams, set the outputUnigrams argument to true.

In all cases, all non-CJK input is passed through unmodified.

Arguments:

han

Optional

Default: true

If false, Han (Chinese) characters will not form bigrams.

hiragana

Optional

Default: true

If false, Hiragana (Japanese) characters will not form bigrams.

katakana

Optional

Default: true

If false, Katakana (Japanese) characters will not form bigrams.

hangul

Optional

Default: true

If false, Hangul (Korean) characters will not form bigrams.

outputUnigrams

Optional

Default: false

If true, in addition to forming bigrams, all characters are also passed through as unigrams.

See the example under Traditional Chinese.

Simplified Chinese

For Simplified Chinese, Solr provides support for Chinese sentence and word segmentation with the HMM Chinese Tokenizer. This component includes a large dictionary and segments Chinese text into words with the Hidden Markov Model. To use this tokenizer, you must enable the analysis-extras Module.

The default configuration of the ICU Tokenizer is also suitable for Simplified Chinese text. It follows the Word Break rules from the Unicode Text Segmentation algorithm for non-Chinese text, and uses a dictionary to segment Chinese words. To use this tokenizer, you must enable the analysis-extras Module.

Also useful for Chinese analysis:

CJK Width Filter folds fullwidth ASCII variants into the equivalent Basic Latin forms, and folds halfwidth Katakana variants into their equivalent fullwidth forms.

Examples:

With name

<analyzer>
  <tokenizer name="hmmChinese"/>
  <filter name="cjkWidth"/>
  <filter name="stop"
          words="org/apache/lucene/analysis/cn/smart/stopwords.txt"/>
  <filter name="porterStem"/>
  <filter name="lowercase"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.HMMChineseTokenizerFactory"/>
  <filter class="solr.CJKWidthFilterFactory"/>
  <filter class="solr.StopFilterFactory"
          words="org/apache/lucene/analysis/cn/smart/stopwords.txt"/>
  <filter class="solr.PorterStemFilterFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
</analyzer>

<analyzer>
  <tokenizer name="icu"/>
  <filter name="cjkWidth"/>
  <filter name="stop"
          words="org/apache/lucene/analysis/cn/smart/stopwords.txt"/>
  <filter name="lowercase"/>
</analyzer>

HMM Chinese Tokenizer

For Simplified Chinese, Solr provides support for Chinese sentence and word segmentation with the solr.HMMChineseTokenizerFactory in the analysis-extras module. This component includes a large dictionary and segments Chinese text into words with the Hidden Markov Model. To use this tokenizer, you must enable the analysis-extras Module.

Factory class: solr.HMMChineseTokenizerFactory

Arguments: None

Examples:

To use the default setup with fallback to English Porter stemmer for English words, use:

<analyzer class="org.apache.lucene.analysis.cn.smart.SmartChineseAnalyzer"/>

Or to configure your own analysis setup, use the solr.HMMChineseTokenizerFactory along with your custom filter setup. See an example of this in the Simplified Chinese section.

Czech

Solr includes a light stemmer for Czech, following this algorithm, and Lucene includes an example stopword list.

Factory class: solr.CzechStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="czechStem"/>
<analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.CzechStemFilterFactory"/>
<analyzer>

In: "prezidenští, prezidenta, prezidentského"

Tokenizer to Filter: "prezidenští", "prezidenta", "prezidentského"

Out: "preziden", "preziden", "preziden"

Danish

Solr can stem Danish using the Snowball Porter Stemmer with an argument of language="Danish".

Also relevant are the Scandinavian normalization filters.

Factory class: solr.SnowballPorterFilterFactory

Arguments:

language

Required

Default: none

The stemmer language, Danish in this case.

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="snowballPorter" language="Danish" />
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Danish" />
</analyzer>

In: "undersøg undersøgelse"

Tokenizer to Filter: "undersøg"(1) "undersøgelse"(2),

Out: "undersøg"(1), "undersøg"(2)

Dutch

Solr can stem Dutch using the Snowball Porter Stemmer with an argument of language="Dutch".

Factory class: solr.SnowballPorterFilterFactory

Arguments:

language

Required

Default: none

The stemmer language, Dutch in this case.

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="snowballPorter" language="Dutch"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Dutch"/>
</analyzer>

In: "kanaal kanalen"

Tokenizer to Filter: "kanaal", "kanalen"

Out: "kanal", "kanal"

Estonian

Solr can stem Estonian using the Snowball Porter Stemmer with an argument of language="Estonian".

Factory class: solr.SnowballPorterFilterFactory

Arguments:

language

Required

Default: none

The stemmer language, Estonian in this case.

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="snowballPorter" language="Estonian"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Estonian"/>
</analyzer>

In: "Taevani tõustes"

Tokenizer to Filter: "Taevani", "tõustes"

Out: "taevani", "tõus"

Finnish

Solr includes support for stemming Finnish, and Lucene includes an example stopword list.

Factory class: solr.FinnishLightStemFilterFactory

Arguments: None

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="finnishLightStem"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.FinnishLightStemFilterFactory"/>
</analyzer>

In: "kala kalat"

Tokenizer to Filter: "kala", "kalat"

Out: "kala", "kala"

French

Elision Filter

Removes article elisions from a token stream. This filter can be useful for languages such as French, Catalan, Italian, and Irish.

Factory class: solr.ElisionFilterFactory

Arguments:

articles

Optional

Default: none

The pathname of a file that contains a list of articles, one per line, to be stripped. Articles are words such as "le", which are commonly abbreviated, such as in l’avion (the plane). This file should include the abbreviated form, which precedes the apostrophe. In this case, simply "l". If no articles attribute is specified, a default set of French articles is used.

ignoreCase

Optional

Default: false

If true, the filter ignores the case of words when comparing them to the common word file.

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="elision"
          ignoreCase="true"
          articles="lang/contractions_fr.txt"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.ElisionFilterFactory"
          ignoreCase="true"
          articles="lang/contractions_fr.txt"/>
</analyzer>

In: "L’histoire d’art"

Tokenizer to Filter: "L’histoire", "d’art"

Out: "histoire", "art"

French Light Stem Filter

Solr includes three stemmers for French: one in the solr.SnowballPorterFilterFactory, a lighter stemmer called solr.FrenchLightStemFilterFactory, and an even less aggressive stemmer called solr.FrenchMinimalStemFilterFactory. Lucene includes an example stopword list.

Factory classes: solr.FrenchLightStemFilterFactory, solr.FrenchMinimalStemFilterFactory

Arguments: None

Examples:

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="elision"
          articles="lang/contractions_fr.txt"/>
  <filter name="frenchLightStem"/>
</analyzer>

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="elision"
          articles="lang/contractions_fr.txt"/>
  <filter name="frenchMinimalStem"/>
</analyzer>

In: "le chat, les chats"

Tokenizer to Filter: "le", "chat", "les", "chats"

Out: "le", "chat", "le", "chat"

Galician

Solr includes a stemmer for Galician following this algorithm, and Lucene includes an example stopword list.

Factory class: solr.GalicianStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="galicianStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.GalicianStemFilterFactory"/>
</analyzer>

In: "felizmente Luzes"

Tokenizer to Filter: "felizmente", "luzes"

Out: "feliz", "luz"

German

Solr includes four stemmers for German: one in the solr.SnowballPorterFilterFactory language="German", a stemmer called solr.GermanStemFilterFactory, a lighter stemmer called solr.GermanLightStemFilterFactory, and an even less aggressive stemmer called solr.GermanMinimalStemFilterFactory. Lucene includes an example stopword list.

Factory classes: solr.GermanStemFilterFactory, solr.LightGermanStemFilterFactory, solr.MinimalGermanStemFilterFactory

Arguments: None

Examples:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="germanStem"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.GermanStemFilterFactory"/>
</analyzer>

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="germanLightStem"/>
</analyzer>

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="germanMinimalStem"/>
</analyzer>

In: "haus häuser"

Tokenizer to Filter: "haus", "häuser"

Out: "haus", "haus"

Greek

This filter converts uppercase letters in the Greek character set to the equivalent lowercase character.

Factory class: solr.GreekLowerCaseFilterFactory

Arguments: None

Use of custom charsets is no longer supported as of Solr 3.1. If you need to index text in these encodings, please use Java’s character set conversion facilities (InputStreamReader, etc.) during I/O, so that Lucene can analyze this text as Unicode instead.

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="greekLowercase"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.GreekLowerCaseFilterFactory"/>
</analyzer>

Hindi

Solr includes support for stemming Hindi following this algorithm (PDF), support for common spelling differences through the solr.HindiNormalizationFilterFactory, support for encoding differences through the solr.IndicNormalizationFilterFactory following this algorithm, and Lucene includes an example stopword list.

Factory classes: solr.IndicNormalizationFilterFactory, solr.HindiNormalizationFilterFactory, solr.HindiStemFilterFactory

Arguments: None

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="indicNormalization"/>
  <filter name="hindiNormalization"/>
  <filter name="hindiStem"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.IndicNormalizationFilterFactory"/>
  <filter class="solr.HindiNormalizationFilterFactory"/>
  <filter class="solr.HindiStemFilterFactory"/>
</analyzer>

Indonesian

Solr includes support for stemming Indonesian (Bahasa Indonesia) following this algorithm (PDF), and Lucene includes an example stopword list.

Factory class: solr.IndonesianStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="indonesianStem" stemDerivational="true" />
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.IndonesianStemFilterFactory" stemDerivational="true" />
</analyzer>

In: "sebagai sebagainya"

Tokenizer to Filter: "sebagai", "sebagainya"

Out: "bagai", "bagai"

Italian

Solr includes two stemmers for Italian: one in the solr.SnowballPorterFilterFactory language="Italian", and a lighter stemmer called solr.ItalianLightStemFilterFactory. Lucene includes an example stopword list.

Factory class: solr.ItalianStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="elision"
          articles="lang/contractions_it.txt"/>
  <filter name="italianLightStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.ElisionFilterFactory"
          articles="lang/contractions_it.txt"/>
  <filter class="solr.ItalianLightStemFilterFactory"/>
</analyzer>

In: "propaga propagare propagamento"

Tokenizer to Filter: "propaga", "propagare", "propagamento"

Out: "propag", "propag", "propag"

Irish

Solr can stem Irish using the Snowball Porter Stemmer with an argument of language="Irish". Solr includes solr.IrishLowerCaseFilterFactory, which can handle Irish-specific constructs. Solr also includes a set of contractions for Irish which can be stripped using solr.ElisionFilterFactory.

Factory class: solr.SnowballPorterFilterFactory

Arguments:

language

Required

Default: none

The stemmer language, Irish in this case.

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="elision"
          articles="lang/contractions_ga.txt"/>
  <filter name="irishLowercase"/>
  <filter name="snowballPorter" language="Irish" />
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.ElisionFilterFactory"
          articles="lang/contractions_ga.txt"/>
  <filter class="solr.IrishLowerCaseFilterFactory"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Irish" />
</analyzer>

In: "siopadóireacht síceapatacha b’fhearr m’athair"

Tokenizer to Filter: "siopadóireacht", "síceapatacha", "b’fhearr", "m’athair"

Out: "siopadóir", "síceapaite", "fearr", "athair"

Japanese

Solr includes support for analyzing Japanese, via the Lucene Kuromoji morphological analyzer, which includes several analysis components - more details on each below:

JapaneseIterationMarkCharFilter normalizes Japanese horizontal iteration marks (odoriji) to their expanded form.
JapaneseTokenizer tokenizes Japanese using morphological analysis, and annotates each term with part-of-speech, base form (a.k.a. lemma), reading and pronunciation.
JapaneseBaseFormFilter replaces original terms with their base forms (a.k.a. lemmas).
JapanesePartOfSpeechStopFilter removes terms that have one of the configured parts-of-speech.
JapaneseKatakanaStemFilter normalizes common katakana spelling variations ending in a long sound character (U+30FC) by removing the long sound character.

Also useful for Japanese analysis, from lucene-analyzers-common:

CJKWidthFilter folds fullwidth ASCII variants into the equivalent Basic Latin forms, and folds halfwidth Katakana variants into their equivalent fullwidth forms.

Japanese Iteration Mark CharFilter

Normalizes horizontal Japanese iteration marks (odoriji) to their expanded form. Vertical iteration marks are not supported.

Factory class: JapaneseIterationMarkCharFilterFactory

Arguments:

normalizeKanji

Optional

Default: true

Set to false to not normalize kanji iteration marks.

normalizeKana

Optional

Default: true

Set to false to not normalize kana iteration marks.

Japanese Tokenizer

Tokenizer for Japanese that uses morphological analysis, and annotates each term with part-of-speech, base form (a.k.a. lemma), reading and pronunciation.

JapaneseTokenizer has a search mode (the default) that does segmentation useful for search: a heuristic is used to segment compound terms into their constituent parts while also keeping the original compound terms as synonyms.

Factory class: solr.JapaneseTokenizerFactory

Arguments:

mode

Optional

Default: none

Use search mode to get a noun-decompounding effect useful for search. search mode improves segmentation for search at the expense of part-of-speech accuracy. Valid values for mode are:

normal: default segmentation
search: segmentation useful for search (extra compound splitting)
extended: search mode plus unigramming of unknown words (experimental)

For some applications it might be good to use search mode for indexing and normal mode for queries to increase precision and prevent parts of compounds from being matched and highlighted.

userDictionary

Optional

Default: none

Filename for a user dictionary, which allows overriding the statistical model with your own entries for segmentation, part-of-speech tags and readings without a need to specify weights. See lang/userdict_ja.txt for a sample user dictionary file.

userDictionaryEncoding

Optional

Default: UTF-8

User dictionary encoding.

discardPunctuation

Optional

Default: true

Set to false to keep punctuation, true to discard.

discardCompoundToken

Optional

Default: none

Set to false to keep original compound tokens with the search mode, true to discard.

Japanese Base Form Filter

Replaces original terms' text with the corresponding base form (lemma). (JapaneseTokenizer annotates each term with its base form.)

Factory class: JapaneseBaseFormFilterFactory

Arguments: None

Japanese Part Of Speech Stop Filter

Removes terms with one of the configured parts-of-speech. JapaneseTokenizer annotates terms with parts-of-speech.

Factory class : JapanesePartOfSpeechStopFilterFactory

Arguments:

tags

Optional

Default: none

Filename for a list of parts-of-speech for which to remove terms. See conf/lang/stoptags_ja.txt in the sample_techproducts_config configset for an example.

Japanese Katakana Stem Filter

Normalizes common katakana spelling variations ending in a long sound character (U+30FC) by removing the long sound character.

solr.CJKWidthFilterFactory should be specified prior to this filter to normalize half-width katakana to full-width.

Factory class: JapaneseKatakanaStemFilterFactory

Arguments:

minimumLength

Optional

Default: 4

Terms below this length will not be stemmed. Value must be 2 or more.

CJK Width Filter

Folds fullwidth ASCII variants into the equivalent Basic Latin forms, and folds halfwidth Katakana variants into their equivalent fullwidth forms.

Factory class: CJKWidthFilterFactory

Arguments: None

Example:

With name

<fieldType name="text_ja" positionIncrementGap="100" autoGeneratePhraseQueries="false">
  <analyzer>
    <!-- Uncomment if you need to handle iteration marks: -->
    <!-- <charFilter name="japaneseIterationMark" /> -->
    <tokenizer name="japanese" mode="search" userDictionary="lang/userdict_ja.txt"/>
    <filter name="japaneseBaseForm"/>
    <filter name="japanesePartOfSpeechStop" tags="lang/stoptags_ja.txt"/>
    <filter name="cjkWidth"/>
    <filter name="stop" ignoreCase="true" words="lang/stopwords_ja.txt"/>
    <filter name="japaneseKatakanaStem" minimumLength="4"/>
    <filter name="lowercase"/>
  </analyzer>
</fieldType>

With class name (legacy)

<fieldType name="text_ja" positionIncrementGap="100" autoGeneratePhraseQueries="false">
  <analyzer>
    <!-- Uncomment if you need to handle iteration marks: -->
    <!-- <charFilter class="solr.JapaneseIterationMarkCharFilterFactory" /> -->
    <tokenizer class="solr.JapaneseTokenizerFactory" mode="search" userDictionary="lang/userdict_ja.txt"/>
    <filter class="solr.JapaneseBaseFormFilterFactory"/>
    <filter class="solr.JapanesePartOfSpeechStopFilterFactory" tags="lang/stoptags_ja.txt"/>
    <filter class="solr.CJKWidthFilterFactory"/>
    <filter class="solr.StopFilterFactory" ignoreCase="true" words="lang/stopwords_ja.txt"/>
    <filter class="solr.JapaneseKatakanaStemFilterFactory" minimumLength="4"/>
    <filter class="solr.LowerCaseFilterFactory"/>
  </analyzer>
</fieldType>

Korean

The Korean (nori) analyzer integrates Lucene’s nori analysis module into Solr. It uses the mecab-ko-dic dictionary to perform morphological analysis of Korean texts.

The dictionary was built with MeCab and defines a format for the features adapted for the Korean language.

Nori also has a user dictionary feature that allows overriding the statistical model with your own entries for segmentation, part-of-speech tags, and readings without a need to specify weights.

Example:

With name

<fieldType name="text_ko" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer name="korean" decompoundMode="discard" outputUnknownUnigrams="false"/>
    <filter name="koreanPartOfSpeechStop" />
    <filter name="koreanReadingForm" />
    <filter name="lowercase" />
  </analyzer>
</fieldType>

With class name (legacy)

<fieldType name="text_ko" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer class="solr.KoreanTokenizerFactory" decompoundMode="discard" outputUnknownUnigrams="false"/>
    <filter class="solr.KoreanPartOfSpeechStopFilterFactory" />
    <filter class="solr.KoreanReadingFormFilterFactory" />
    <filter class="solr.LowerCaseFilterFactory" />
  </analyzer>
</fieldType>

Korean Tokenizer

Factory class: solr.KoreanTokenizerFactory

SPI name: korean

Arguments:

userDictionary

Optional

Default: none

Path to a user-supplied dictionary to add custom nouns or compound terms to the default dictionary.

userDictionaryEncoding

Optional

Default: none

Character encoding of the user dictionary.

decompoundMode

Optional

Default: discard

Defines how to handle compound tokens. The options are:

none: No decomposition for tokens.
discard: Tokens are decomposed and the original form is discarded.
mixed: Tokens are decomposed and the original form is retained.

outputUnknownUnigrams

Optional

Default: false

If true, unigrams will be output for unknown words.

discardPunctuation

Optional

Default: true

If true, punctuation will be discarded.

Korean Part of Speech Stop Filter

This filter removes tokens that match parts of speech tags.

Factory class: solr.KoreanPartOfSpeechStopFilterFactory

SPI name: koreanPartOfSpeechStop

Arguments: None.

Korean Reading Form Filter

This filter replaces term text with the Reading Attribute, the Hangul transcription of Hanja characters.

Factory class: solr.KoreanReadingFormFilterFactory

SPI name: koreanReadingForm

Arguments: None.

Hebrew, Lao, Myanmar, Khmer

Lucene provides support, in addition to UAX#29 word break rules, for Hebrew’s use of the double and single quote characters, and for segmenting Lao, Myanmar, and Khmer into syllables with the solr.ICUTokenizerFactory in the analysis-extras module. To use this tokenizer, you must enable the analysis-extras Module.

See ICUTokenizer for more information.

Latvian

Solr includes support for stemming Latvian, and Lucene includes an example stopword list.

Factory class: solr.LatvianStemFilterFactory

Arguments: None

Example:

With name

<fieldType name="text_lvstem" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer name="standard"/>
    <filter name="lowercase"/>
    <filter name="latvianStem"/>
  </analyzer>
</fieldType>

With class name (legacy)

<fieldType name="text_lvstem" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer class="solr.StandardTokenizerFactory"/>
    <filter class="solr.LowerCaseFilterFactory"/>
    <filter class="solr.LatvianStemFilterFactory"/>
  </analyzer>
</fieldType>

In: "tirgiem tirgus"

Tokenizer to Filter: "tirgiem", "tirgus"

Out: "tirg", "tirg"

Norwegian

Solr includes two classes for stemming Norwegian, NorwegianLightStemFilterFactory and NorwegianMinimalStemFilterFactory. Lucene includes an example stopword list.

Another option is to use the Snowball Porter Stemmer with an argument of language="Norwegian".

For normalization, there is a NorwegianNormalizationFilterFactory which is a variant of the Scandinavian normalization filters but with folding rules tuned for Norwegian.

Norwegian Light Stemmer

The NorwegianLightStemFilterFactory requires a "two-pass" sort for the -dom and -het endings. This means that in the first pass the word "kristendom" is stemmed to "kristen", and then all the general rules apply so it will be further stemmed to "krist". The effect of this is that "kristen," "kristendom," "kristendommen," and "kristendommens" will all be stemmed to "krist."

The second pass is to pick up -dom and -het endings. Consider this example:

One pass

Two passes

Before

After

Before

After

forlegen

forleg

forlegen

forleg

forlegenhet

forlegen

forlegenhet

forleg

forlegenheten

forlegen

forlegenheten

forleg

forlegenhetens

forlegen

forlegenhetens

forleg

firkantet

firkant

firkantet

firkant

firkantethet

firkantet

firkantethet

firkant

firkantetheten

firkantet

firkantetheten

firkant

Factory class: solr.NorwegianLightStemFilterFactory

Arguments:

variant

Optional

Default: nb

The Norwegian language variant to use. Valid values are:

nb: Bokmål
nn: Nynorsk
no: both

Example:

With name

<fieldType name="text_no" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer name="standard"/>
    <filter name="lowercase"/>
    <filter name="stop" ignoreCase="true" words="lang/stopwords_no.txt" format="snowball"/>
    <filter name="norwegianLightStem"/>
  </analyzer>
</fieldType>

With class name (legacy)

<fieldType name="text_no" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer class="solr.StandardTokenizerFactory"/>
    <filter class="solr.LowerCaseFilterFactory"/>
    <filter class="solr.StopFilterFactory" ignoreCase="true" words="lang/stopwords_no.txt" format="snowball"/>
    <filter class="solr.NorwegianLightStemFilterFactory"/>
  </analyzer>
</fieldType>

In: "Forelskelsen"

Tokenizer to Filter: "forelskelsen"

Out: "forelske"

Norwegian Minimal Stemmer

The NorwegianMinimalStemFilterFactory stems plural forms of Norwegian nouns only.

Factory class: solr.NorwegianMinimalStemFilterFactory

Arguments:

variant

Optional

Default: nb

The Norwegian language variant to use. Valid values are:

nb: Bokmål
nn: Nynorsk
no: both

Example:

<fieldType name="text_no" class="solr.TextField" positionIncrementGap="100">
  <analyzer>
    <tokenizer name="standard"/>
    <filter name="lowercase"/>
    <filter name="stop" ignoreCase="true" words="lang/stopwords_no.txt" format="snowball"/>
    <filter name="norwegianMinimalStem"/>
  </analyzer>
</fieldType>

In: "Bilens"

Tokenizer to Filter: "bilens"

Out: "bil"

Norwegian Normalization Filter

This filter normalize use of the interchangeable Scandinavian characters æÆäÄöÖøØåÅ and folded variants (ae, oe and aa) by transforming them to æÆøØåÅ. This is a variant of ScandinavianNormalizationFilter, with folding rules customized for Norwegian.

Factory class: solr.NorwegianNormalizationFilterFactory

Arguments: None

Example:

With name

<analyzer>
<tokenizer name="standard"/>
<filter name="lowercase"/>
<filter name="norwegianNormalization"/>
</analyzer>

With class name (legacy)

<analyzer>
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.LowerCaseFilterFactory"/>
<filter class="solr.NorwegianNormalizationFilterFactory"/>
</analyzer>

In: "blåbærsyltetøj blåbärsyltetöj blaabaarsyltetoej blabarsyltetoj"

Tokenizer to Filter: "blåbærsyltetøy", "blåbärsyltetöy", "blaabaersyltetoey", "blabarsyltetoy"

Out: "blåbærsyltetøy", "blåbærsyltetøy", "blåbærsyltetøy", "blabarsyltetoy"

Persian

Persian Filter Factories

Solr includes support for normalizing Persian, and Lucene includes an example stopword list.

Factory class: solr.PersianNormalizationFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="arabicNormalization"/>
  <filter name="persianNormalization"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.ArabicNormalizationFilterFactory"/>
  <filter class="solr.PersianNormalizationFilterFactory"/>
</analyzer>

Polish

Solr provides support for Polish stemming with the solr.StempelPolishStemFilterFactory, and solr.MorphologikFilterFactory for lemmatization, in the analysis-extras module. The solr.StempelPolishStemFilterFactory component includes an algorithmic stemmer with tables for Polish. To use this tokenizer, you must enable the analysis-extras Module.

Factory class: solr.StempelPolishStemFilterFactory and solr.MorfologikFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="stempelPolishStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.StempelPolishStemFilterFactory"/>
</analyzer>

<analyzer>
  <tokenizer name="standard"/>
  <filter name="morfologik" dictionary="morfologik/stemming/polish/polish.dict"/>
  <filter name="lowercase"/>
</analyzer>

In: ""studenta studenci"

Tokenizer to Filter: "studenta", "studenci"

Out: "student", "student"

More information about the Stempel stemmer is available in the Lucene javadocs.

Note the lower case filter is applied after the Morfologik stemmer; this is because the Polish dictionary contains proper names and then proper term case may be important to resolve disambiguities (or even lookup the correct lemma at all).

The Morfologik dictionary parameter value is a constant specifying which dictionary to choose. The dictionary resource must be named path/to/language.dict and have an associated .info metadata file. See the Morfologik project for details. If the dictionary attribute is not provided, the Polish dictionary is loaded and used by default.

Portuguese

Solr includes four stemmers for Portuguese: one in the solr.SnowballPorterFilterFactory, an alternative stemmer called solr.PortugueseStemFilterFactory, a lighter stemmer called solr.PortugueseLightStemFilterFactory, and an even less aggressive stemmer called solr.PortugueseMinimalStemFilterFactory. Lucene includes an example stopword list.

Factory classes: solr.PortugueseStemFilterFactory, solr.PortugueseLightStemFilterFactory, solr.PortugueseMinimalStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="portugueseStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.PortugueseStemFilterFactory"/>
</analyzer>

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="portugueseLightStem"/>
</analyzer>

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="portugueseMinimalStem"/>
</analyzer>

In: "praia praias"

Tokenizer to Filter: "praia", "praias"

Out: "pra", "pra"

Romanian

Solr can stem Romanian using the Snowball Porter Stemmer with an argument of language="Romanian".

Factory class: solr.SnowballPorterFilterFactory

Arguments:

language

Required

Default: none

The stemmer language, Romanian in this case.

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="wnowballPorter" language="Romanian" />
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Romanian" />
</analyzer>

Russian

Russian Stem Filter

Solr includes two stemmers for Russian: one in the solr.SnowballPorterFilterFactory language="Russian", and a lighter stemmer called solr.RussianLightStemFilterFactory. Lucene includes an example stopword list.

Factory class: solr.RussianLightStemFilterFactory

Arguments: None

Example:

With name

<analyzer type="index">
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="russianLightStem"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.RussianLightStemFilterFactory"/>
</analyzer>

Scandinavian

Scandinavian is a language group spanning three languages Norwegian, Swedish and Danish which are very similar.

Swedish å, ä, ö are in fact the same letters as Norwegian and Danish å, æ, ø and thus interchangeable when used between these languages. They are however folded differently when people type them on a keyboard lacking these characters.

In that situation almost all Swedish people use a, a, o instead of å, ä, ö. Norwegians and Danes on the other hand usually type aa, ae and oe instead of å, æ and ø. Some do however use a, a, o, oo, ao and sometimes permutations of everything above.

There are two filters for helping with normalization between Scandinavian languages: one is solr.ScandinavianNormalizationFilterFactory trying to preserve the special characters (æäöå) and another solr.ScandinavianFoldingFilterFactory which folds these to the more broad ø/ö → o, etc.

See also each language section for other relevant filters.

Scandinavian Normalization Filter

This filter normalize use of the interchangeable Scandinavian characters æÆäÄöÖøØ and folded variants (aa, ao, ae, oe and oo) by transforming them to åÅæÆøØ.

It’s a semantically less destructive solution than ScandinavianFoldingFilter, most useful when a person with a Norwegian or Danish keyboard queries a Swedish index and vice versa. This filter does not perform the common Swedish folds of å and ä to a nor ö to o.

Factory class: solr.ScandinavianNormalizationFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="scandinavianNormalization"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.ScandinavianNormalizationFilterFactory"/>
</analyzer>

In: "blåbærsyltetøj blåbärsyltetöj blaabaarsyltetoej blabarsyltetoj"

Tokenizer to Filter: "blåbærsyltetøj", "blåbärsyltetöj", "blaabaersyltetoej", "blabarsyltetoj"

Out: "blåbærsyltetøj", "blåbærsyltetøj", "blåbærsyltetøj", "blabarsyltetoj"

Scandinavian Folding Filter

This filter folds Scandinavian characters åÅäæÄÆ → a and öÖøØ → o. It also discriminate against use of double vowels aa, ae, ao, oe and oo, leaving just the first one.

It’s a semantically more destructive solution than ScandinavianNormalizationFilter, but can in addition help with matching raksmorgas as räksmörgås.

Factory class: solr.ScandinavianFoldingFilterFactory

Arguments: None

Example:

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="scandinavianFolding"/>
</analyzer>

In: "blåbærsyltetøj blåbärsyltetöj blaabaarsyltetoej blabarsyltetoj"

Tokenizer to Filter: "blåbærsyltetøj", "blåbärsyltetöj", "blaabaersyltetoej", "blabarsyltetoj"

Out: "blabarsyltetoj", "blabarsyltetoj", "blabarsyltetoj", "blabarsyltetoj"

Serbian

Serbian Normalization Filter

Solr includes a filter that normalizes Serbian Cyrillic and Latin characters. Note that this filter only works with lowercased input.

For user tips & advice on using this filter, see Serbian Language Support in the Solr Wiki.

Factory class: solr.SerbianNormalizationFilterFactory

Arguments:

haircut

Optional

Default: bald

Select the extend of normalization. Valid values are:

bald: Cyrillic characters are first converted to Latin; then, Latin characters have their diacritics removed, with the exception of LATIN SMALL LETTER D WITH STROKE (U+0111) which is converted to “dj”
regular: Only Cyrillic to Latin normalization will be applied, preserving the Latin diatrics

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="serbianNormalization" haircut="bald"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SerbianNormalizationFilterFactory" haircut="bald"/>
</analyzer>

Spanish

Solr includes two stemmers for Spanish: one in the solr.SnowballPorterFilterFactory language="Spanish", and a lighter stemmer called solr.SpanishLightStemFilterFactory. Lucene includes an example stopword list.

Factory class: solr.SpanishStemFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="spanishLightStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SpanishLightStemFilterFactory"/>
</analyzer>

In: "torear toreara torearlo"

Tokenizer to Filter: "torear", "toreara", "torearlo"

Out: "tor", "tor", "tor"

Swedish

Swedish Stem Filter

Solr includes three stemmers for Swedish: one in the solr.SnowballPorterFilterFactory language="Swedish", a lighter stemmer called solr.SwedishLightStemFilterFactory, and a minimal stemmer solr.SwedishMinimalStemFilterFactory.

The Light variant is based on simple rules, and removes suffixes like -het, -heten, -else, -elser etc., while the Minimal one only tries to normalize singular/plural endings like -er, -ar, -arne etc. See the Lucene javadocs for more information.

The Swedish Light and Minimal stemmers are known to produce many conflicting word stems, significantly hurting search precision. It may be necessary to provide an extensive list of custom stemmer mappings to counteract this, e.g. using the StemmerOverrideFilter.

Lucene includes an example stopword list.

Also relevant are the Scandinavian normalization filters.

Factory class: solr.SwedishStemFilterFactory, solr.SwedishLightStemFilterFactory and solr.SwedishMinimalStemFilterFactory.

Arguments: None

Example (SwedishLightStemFilterFactory):

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="lowercase"/>
  <filter name="swedishLightStem"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.SwedishLightStemFilterFactory"/>
</analyzer>

In: "kloke klokhet klokheten"

Tokenizer to Filter: "kloke", "klokhet", "klokheten"

Out: "klok", "klok", "klok"

Thai

This filter converts sequences of Thai characters into individual Thai words. Unlike European languages, Thai does not use whitespace to delimit words.

Factory class: solr.ThaiTokenizerFactory

Arguments: None

Example:

With name

<analyzer type="index">
  <tokenizer name="thai"/>
  <filter name="lowercase"/>
</analyzer>

With class name (legacy)

<analyzer type="index">
  <tokenizer class="solr.ThaiTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
</analyzer>

Turkish

Solr includes support for stemming Turkish with the solr.SnowballPorterFilterFactory; support for case-insensitive search with the solr.TurkishLowerCaseFilterFactory; support for stripping apostrophes and following suffixes with solr.ApostropheFilterFactory (see Role of Apostrophes in Turkish Information Retrieval); support for a form of stemming that truncating tokens at a configurable maximum length through the solr.TruncateTokenFilterFactory (see Information Retrieval on Turkish Texts); and Lucene includes an example stopword list.

Factory class: solr.TurkishLowerCaseFilterFactory

Arguments: None

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="apostrophe"/>
  <filter name="turkishLowercase"/>
  <filter name="snowballPorter" language="Turkish"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.ApostropheFilterFactory"/>
  <filter class="solr.TurkishLowerCaseFilterFactory"/>
  <filter class="solr.SnowballPorterFilterFactory" language="Turkish"/>
</analyzer>

Another example, illustrating diacritics-insensitive search:

<analyzer>
  <tokenizer name="standard"/>
  <filter name="apostrophe"/>
  <filter name="turkishLowercase"/>
  <filter name="asciiFoldingFilterFactory" preserveOriginal="true"/>
  <filter name="keywordRepeat"/>
  <filter name="truncate" prefixLength="5"/>
  <filter name="removeDuplicates"/>
</analyzer>

Ukrainian

Solr provides support for Ukrainian lemmatization with the solr.MorphologikFilterFactory, in the analysis-extras module. To use this filter, you must enable the analysis-extras Module.

Lucene also includes an example Ukrainian stopword list, in the lucene-analyzers-morfologik jar.

Factory class: solr.MorfologikFilterFactory

Arguments:

dictionary

Required

Default: none

The path to a lemmatizer dictionary. The lucene-analyzers-morfologik jar contains a Ukrainian dictionary at org/apache/lucene/analysis/uk/ukrainian.dict.

Example:

With name

<analyzer>
  <tokenizer name="standard"/>
  <filter name="stop" words="org/apache/lucene/analysis/uk/stopwords.txt"/>
  <filter name="lowercase"/>
  <filter name="morfologik" dictionary="org/apache/lucene/analysis/uk/ukrainian.dict"/>
</analyzer>

With class name (legacy)

<analyzer>
  <tokenizer class="solr.StandardTokenizerFactory"/>
  <filter class="solr.StopFilterFactory" words="org/apache/lucene/analysis/uk/stopwords.txt"/>
  <filter class="solr.LowerCaseFilterFactory"/>
  <filter class="solr.MorfologikFilterFactory" dictionary="org/apache/lucene/analysis/uk/ukrainian.dict"/>
</analyzer>

The Morfologik dictionary parameter value is a constant specifying which dictionary to choose. The dictionary resource must be named path/to/language.dict and have an associated .info metadata file. See the Morfologik project for details. If the dictionary attribute is not provided, the Polish dictionary is loaded and used by default.

Analysis Extras Module

Many of the language features listed above are supported by the analysis-extras Solr Module that needs to be enabled before use.

Additional details on the specific jar files required can be found in the Module’s README.