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Tokenizers
The tokenizer module in Nominatim is responsible for analysing the names given to OSM objects and the terms of an incoming query in order to make sure, they can be matched appropriately.
Nominatim offers different tokenizer modules, which behave differently and have different configuration options. This sections describes the tokenizers and how they can be configured.
!!! important The use of a tokenizer is tied to a database installation. You need to choose and configure the tokenizer before starting the initial import. Once the import is done, you cannot switch to another tokenizer anymore. Reconfiguring the chosen tokenizer is very limited as well. See the comments in each tokenizer section.
ICU tokenizer
The ICU tokenizer uses the ICU library to normalize names and queries. It also offers configurable decomposition and abbreviation handling. This tokenizer is currently the default.
To enable the tokenizer add the following line to your project configuration:
NOMINATIM_TOKENIZER=icu
How it works
On import the tokenizer processes names in the following three stages:
- During the Sanitizer step incoming names are cleaned up and converted to full names. This step can be used to regularize spelling, split multi-name tags into their parts and tag names with additional attributes. See the Sanitizers section below for available cleaning routines.
- The Normalization part removes all information from the full names that are not relevant for search.
- The Token analysis step takes the normalized full names and creates all transliterated variants under which the name should be searchable. See the Token analysis section below for more information.
During query time, only normalization and transliteration are relevant. An incoming query is first split into name chunks (this usually means splitting the string at the commas) and the each part is normalised and transliterated. The result is used to look up places in the search index.
Configuration
The ICU tokenizer is configured using a YAML file which can be configured using
NOMINATIM_TOKENIZER_CONFIG. The configuration is read on import and then
saved as part of the internal database status. Later changes to the variable
have no effect.
Here is an example configuration file:
normalization:
- ":: lower ()"
- "ß > 'ss'" # German szet is unambiguously equal to double ss
transliteration:
- !include /etc/nominatim/icu-rules/extended-unicode-to-asccii.yaml
- ":: Ascii ()"
sanitizers:
- step: split-name-list
token-analysis:
- analyzer: generic
variants:
- !include icu-rules/variants-ca.yaml
- words:
- road -> rd
- bridge -> bdge,br,brdg,bri,brg
mutations:
- pattern: 'ä'
replacements: ['ä', 'ae']
The configuration file contains four sections:
normalization, transliteration, sanitizers and token-analysis.
Normalization and Transliteration
The normalization and transliteration sections each define a set of ICU rules that are applied to the names.
The normalization rules are applied after sanitation. They should remove any information that is not relevant for search at all. Usual rules to be applied here are: lower-casing, removing of special characters, cleanup of spaces.
The transliteration rules are applied at the end of the tokenization process to transfer the name into an ASCII representation. Transliteration can be useful to allow for further fuzzy matching, especially between different scripts.
Each section must contain a list of
ICU transformation rules.
The rules are applied in the order in which they appear in the file.
You can also include additional rules from external yaml file using the
!include tag. The included file must contain a valid YAML list of ICU rules
and may again include other files.
!!! warning The ICU rule syntax contains special characters that conflict with the YAML syntax. You should therefore always enclose the ICU rules in double-quotes.
Sanitizers
The sanitizers section defines an ordered list of functions that are applied to the name and address tags before they are further processed by the tokenizer. They allows to clean up the tagging and bring it to a standardized form more suitable for building the search index.
!!! hint Sanitizers only have an effect on how the search index is built. They do not change the information about each place that is saved in the database. In particular, they have no influence on how the results are displayed. The returned results always show the original information as stored in the OpenStreetMap database.
Each entry contains information of a sanitizer to be applied. It has a
mandatory parameter step which gives the name of the sanitizer. Depending
on the type, it may have additional parameters to configure its operation.
The order of the list matters. The sanitizers are applied exactly in the order that is configured. Each sanitizer works on the results of the previous one.
The following is a list of sanitizers that are shipped with Nominatim.
split-name-list
::: nominatim_db.tokenizer.sanitizers.split_name_list options: members: False heading_level: 6 docstring_section_style: spacy
strip-brace-terms
::: nominatim_db.tokenizer.sanitizers.strip_brace_terms options: members: False heading_level: 6 docstring_section_style: spacy
tag-analyzer-by-language
::: nominatim_db.tokenizer.sanitizers.tag_analyzer_by_language options: members: False heading_level: 6 docstring_section_style: spacy
clean-housenumbers
::: nominatim_db.tokenizer.sanitizers.clean_housenumbers options: members: False heading_level: 6 docstring_section_style: spacy
clean-postcodes
::: nominatim_db.tokenizer.sanitizers.clean_postcodes options: members: False heading_level: 6 docstring_section_style: spacy
clean-tiger-tags
::: nominatim_db.tokenizer.sanitizers.clean_tiger_tags options: members: False heading_level: 6 docstring_section_style: spacy
delete-tags
::: nominatim_db.tokenizer.sanitizers.delete_tags options: members: False heading_level: 6 docstring_section_style: spacy
tag-japanese
::: nominatim_db.tokenizer.sanitizers.tag_japanese options: members: False heading_level: 6 docstring_section_style: spacy
Token Analysis
Token analyzers take a full name and transform it into one or more normalized form that are then saved in the search index. In its simplest form, the analyzer only applies the transliteration rules. More complex analyzers create additional spelling variants of a name. This is useful to handle decomposition and abbreviation.
The ICU tokenizer may use different analyzers for different names. To select
the analyzer to be used, the name must be tagged with the analyzer attribute
by a sanitizer (see for example the
tag-analyzer-by-language sanitizer).
The token-analysis section contains the list of configured analyzers. Each
analyzer must have an id parameter that uniquely identifies the analyzer.
The only exception is the default analyzer that is used when no special
analyzer was selected. There are analysers with special ids:
- '@housenumber'. If an analyzer with that name is present, it is used for normalization of house numbers.
- '@potcode'. If an analyzer with that name is present, it is used for normalization of postcodes.
Different analyzer implementations may exist. To select the implementation,
the analyzer parameter must be set. The different implementations are
described in the following.
Generic token analyzer
The generic analyzer generic is able to create variants from a list of given
abbreviation and decomposition replacements and introduce spelling variations.
Variants
The optional 'variants' section defines lists of replacements which create alternative spellings of a name. To create the variants, a name is scanned from left to right and the longest matching replacement is applied until the end of the string is reached.
The variants section must contain a list of replacement groups. Each group defines a set of properties that describes where the replacements are applicable. In addition, the word section defines the list of replacements to be made. The basic replacement description is of the form:
<source>[,<source>[...]] => <target>[,<target>[...]]
The left side contains one or more source terms to be replaced. The right side
lists one or more replacements. Each source is replaced with each replacement
term.
!!! tip The source and target terms are internally normalized using the normalization rules given in the configuration. This ensures that the strings match as expected. In fact, it is better to use unnormalized words in the configuration because then it is possible to change the rules for normalization later without having to adapt the variant rules.
Decomposition
In its standard form, only full words match against the source. There is a special notation to match the prefix and suffix of a word:
- ~strasse => str # matches "strasse" as full word and in suffix position
- hinter~ => hntr # matches "hinter" as full word and in prefix position
There is no facility to match a string in the middle of the word. The suffix and prefix notation automatically trigger the decomposition mode: two variants are created for each replacement, one with the replacement attached to the word and one separate. So in above example, the tokenization of "hauptstrasse" will create the variants "hauptstr" and "haupt str". Similarly, the name "rote strasse" triggers the variants "rote str" and "rotestr". By having decomposition work both ways, it is sufficient to create the variants at index time. The variant rules are not applied at query time.
To avoid automatic decomposition, use the '|' notation:
- ~strasse |=> str
simply changes "hauptstrasse" to "hauptstr" and "rote strasse" to "rote str".
Initial and final terms
It is also possible to restrict replacements to the beginning and end of a name:
- ^south => s # matches only at the beginning of the name
- road$ => rd # matches only at the end of the name
So the first example would trigger a replacement for "south 45th street" but not for "the south beach restaurant".
Replacements vs. variants
The replacement syntax source => target works as a pure replacement. It changes
the name instead of creating a variant. To create an additional version, you'd
have to write source => source,target. As this is a frequent case, there is
a shortcut notation for it:
<source>[,<source>[...]] -> <target>[,<target>[...]]
The simple arrow causes an additional variant to be added. Note that decomposition has an effect here on the source as well. So a rule
- "~strasse -> str"
means that for a word like hauptstrasse four variants are created:
hauptstrasse, haupt strasse, hauptstr and haupt str.
Mutations
The 'mutation' section in the configuration describes an additional set of replacements to be applied after the variants have been computed.
Each mutation is described by two parameters: pattern and replacements.
The pattern must contain a single regular expression to search for in the
variant name. The regular expressions need to follow the syntax for
Python regular expressions.
Capturing groups are not permitted.
replacements must contain a list of strings that the pattern
should be replaced with. Each occurrence of the pattern is replaced with
all given replacements. Be mindful of combinatorial explosion of variants.
Modes
The generic analyser supports a special mode variant-only. When configured
then it consumes the input token and emits only variants (if any exist). Enable
the mode by adding:
mode: variant-only
to the analyser configuration.
Housenumber token analyzer
The analyzer housenumbers is purpose-made to analyze house numbers. It
creates variants with optional spaces between numbers and letters. Thus,
house numbers of the form '3 a', '3A', '3-A' etc. are all considered equivalent.
The analyzer cannot be customized.
Postcode token analyzer
The analyzer postcodes is pupose-made to analyze postcodes. It supports
a 'lookup' variant of the token, which produces variants with optional
spaces. Use together with the clean-postcodes sanitizer.
The analyzer cannot be customized.
Reconfiguration
Changing the configuration after the import is currently not possible, although this feature may be added at a later time.