Language dictionaries
It's easy to add new languages/synonyms to libpostal by modifying a few text files. The format of each dictionary file roughly resembles a Lucene/Elasticsearch synonyms file:
drive|dr
street|st|str
road|rd
The leftmost string is treated as the canonical/normalized version. Synonyms if any, are appended to the right, delimited by the pipe character.
The supported languages can be found in the resources/dictionaries.
Each language can define one or more dictionaries (sometimes called "gazetteers" in NLP) to help with address parsing, and normalizing abbreviations. The dictionary types are:
- academic_degrees.txt: for post-nominal strings like "M.D.", "Ph.D.", etc.
- ambiguous_expansions.txt: e.g. "E" could be expanded to "East" but could be "E Street", so if the string is encountered, it can either be left alone or expanded. In general, single-letter abbreviations in most languages should also be added to ambiguous_expansions.txt since single letters are also often initials
- building_types.txt: strings indicating a building/house
- company_types.txt: company suffixes like "Inc" or "GmbH"
- concatenated_prefixes_separable.txt: things like "Hinter..." which can be written either concatenated or as separate tokens
- concatenated_suffixes_inseparable.txt: Things like "...bg." => "...burg" where the suffix cannot be separated from the main token, but either has an abbreviated equivalent or simply can help identify the token in parsing as, say, part of a street name
- concatenated_suffixes_separable.txt: Things like "...straße" where the suffix can be either concatenated to the main token or separated
- directionals.txt: strings indicating directions (cardinal and lower/central/upper, etc.)
- level_types.txt: strings indicating a particular floor
- no_number.txt: strings like "no fixed address"
- nulls.txt: strings meaning "not applicable"
- personal_suffixes.txt: post-nominal suffixes, usually generational like Jr/Sr
- personal_titles.txt: civilian, royal and military titles
- place_names.txt: strings found in names of places e.g. "theatre", "aquarium", "restaurant". Nominatim Special Phrases is a great resource for this.
- post_office.txt: strings like "p.o. box"
- qualifiers.txt: strings like "township"
- stopwords.txt: prepositions and articles mostly, very common words which may be ignored in some contexts
- street_types.txt: words like "street", "road", "drive" which indicate a thoroughfare and their respective abbreviations.
- synonyms.txt: any miscellaneous synonyms/abbreviations e.g. "bros" expands to "brothers", etc. These have no special meaning and will essentially just be treated as string replacement.
- toponyms.txt: abbreviations for certain abbreviations relating to toponyms like regions, places, etc. Note: GeoNames covers most of these. In most cases better to leave these alone
- unit_types.txt: strings indicating an apartment or unit number
Most of the dictionaries have been derived using the following process:
- Tokenize every street/venue name in OSM for language x using libpostal
- Count the most common tokens
- Use the Apriori algorithm to extract multi-word phrases
- Run the most frequent words/phrases through Google Translate
- Add the ones that mean "street" (or other relevant words) to dictionaries
- Augment by researching addresses in countries speaking language x
Contributing
If you're a native speaker of one or more languages in libpostal, we'd love your contribution! It's as simple as editing the text files under this directory and submitting a pull request. Dictionaries are organized by language code, so feel free to find any language you speak and start editing! If you don't see your language, just add a directory - there's no additional configuration needed.
To get started adding new language dictionaries or improving support for existing languages, check out the address_languages repo, where we've published lists of 1-5 word phrases found in street/venue names in every language in OSM.
In the future it might be beneficial to move these dictionaries to a wiki so they can be crowdsourced by native speakers regardless of whether or not they use git.