A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.

libpostal: international street address NLP

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libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. The goal of this project is to understand location-based strings in every language, everywhere. For a more comprehensive overview of the research behind libpostal, be sure to check out the (lengthy) introductory blog posts:

🇧🇷 🇫🇮 🇳🇬 🇯🇵 🇽🇰 🇧🇩 🇵🇱 🇻🇳 🇧🇪 🇲🇦 🇺🇦 🇯🇲 🇷🇺 🇮🇳 🇱🇻 🇧🇴 🇩🇪 🇸🇳 🇦🇲 🇰🇷 🇳🇴 🇲🇽 🇨🇿 🇹🇷 🇪🇸 🇸🇸 🇪🇪 🇧🇭 🇳🇱 🇨🇳 🇵🇹 🇵🇷 🇬🇧 🇵🇸

Addresses and the locations they represent are essential for any application dealing with maps (place search, transportation, on-demand/delivery services, check-ins, reviews). Yet even the simplest addresses are packed with local conventions, abbreviations and context, making them difficult to index/query effectively with traditional full-text search engines. This library helps convert the free-form addresses that humans use into clean normalized forms suitable for machine comparison and full-text indexing. Though libpostal is not itself a full geocoder, it can be used as a preprocessing step to make any geocoding application smarter, simpler, and more consistent internationally.

🇷🇴 🇬🇭 🇦🇺 🇲🇾 🇭🇷 🇭🇹 🇺🇸 🇿🇦 🇷🇸 🇨🇱 🇮🇹 🇰🇪 🇨🇭 🇨🇺 🇸🇰 🇦🇴 🇩🇰 🇹🇿 🇦🇱 🇨🇴 🇮🇱 🇬🇹 🇫🇷 🇵🇭 🇦🇹 🇱🇨 🇮🇸 🇮🇩 🇦🇪 🇸🇰 🇹🇳 🇰🇭 🇦🇷 🇭🇰

The core library is written in pure C. Language bindings for Python, Ruby, Go, Java, PHP, and NodeJS are officially supported and it's easy to write bindings in other languages.

Sponsors

If your company is using libpostal, consider asking your organization to sponsor the project. Interpreting what humans mean when they refer to locations is far from a solved problem, and sponsorships help us pursue new frontiers in geospatial NLP. As a sponsor, your company logo will appear prominently on the Github repo page along with a link to your site. Sponsorship info

Backers

Individual users can also help support open geo NLP research by making a monthly donation:

Installation (Mac/Linux)

Before you install, make sure you have the following prerequisites:

On Ubuntu/Debian

sudo apt-get install curl autoconf automake libtool pkg-config

On CentOS/RHEL

sudo yum install curl autoconf automake libtool pkgconfig

On Mac OSX

brew install curl autoconf automake libtool pkg-config

Then to install the C library:

git clone https://github.com/openvenues/libpostal
cd libpostal
./bootstrap.sh
./configure --datadir=[...some dir with a few GB of space...]
make -j4
sudo make install

# On Linux it's probably a good idea to run
sudo ldconfig

libpostal has support for pkg-config, so you can use the pkg-config to print the flags needed to link your program against it:

pkg-config --cflags libpostal         # print compiler flags
pkg-config --libs libpostal           # print linker flags
pkg-config --cflags --libs libpostal  # print both

For example, if you write a program called app.c, you can compile it like this:

gcc app.c `pkg-config --cflags --libs libpostal`

Installation (Windows)

MSys2/MinGW

For Windows the build procedure currently requires MSys2 and MinGW. This can be downloaded from http://msys2.org. Please follow the instructions on the MSys2 website for installation.

Please ensure Msys2 is up-to-date by running:

pacman -Syu

Install the following prerequisites:

pacman -S autoconf automake curl git make libtool gcc mingw-w64-x86_64-gcc

Then to build the C library:

git clone https://github.com/openvenues/libpostal
cd libpostal
cp -rf windows/* ./
./bootstrap.sh
./configure --datadir=[...some dir with a few GB of space...]
make -j4
make install

Notes: When setting the datadir, the C: drive would be entered as /c. The libpostal build script automatically add libpostal on the end of the path, so '/c' would become C:\libpostal\ on Windows.

The compiled .dll will be in the src/.libs/ directory and should be called libpostal-1.dll.

If you require a .lib import library to link this to your application. You can generate one using the Visual Studio lib.exe tool and the libpostal.def definition file:

lib.exe /def:libpostal.def /out:libpostal.lib /machine:x64

Examples of parsing

libpostal's international address parser uses machine learning (Conditional Random Fields) and is trained on over 1 billion addresses in every inhabited country on Earth. We use OpenStreetMap and OpenAddresses as sources of structured addresses, and the OpenCage address format templates at: https://github.com/OpenCageData/address-formatting to construct the training data, supplementing with containing polygons, and generating sub-building components like apartment/floor numbers and PO boxes. We also add abbreviations, drop out components at random, etc. to make the parser as robust as possible to messy real-world input.

These example parse results are taken from the interactive address_parser program that builds with libpostal when you run make. Note that the parser can handle commas vs. no commas as well as various casings and permutations of components (if the input is e.g. just city or just city/postcode).

parser

The parser achieves very high accuracy on held-out data, currently 99.45% correct full parses (meaning a 1 in the numerator for getting every token in the address correct).

Usage (parser)

Here's an example of the parser API using the Python bindings:

from postal.parser import parse_address
parse_address('The Book Club 100-106 Leonard St Shoreditch London EC2A 4RH, United Kingdom')

And an example with the C API:

#include <stdio.h>
#include <stdlib.h>
#include <libpostal/libpostal.h>

int main(int argc, char **argv) {
    // Setup (only called once at the beginning of your program)
    if (!libpostal_setup() || !libpostal_setup_parser()) {
        exit(EXIT_FAILURE);
    }

    libpostal_address_parser_options_t options = libpostal_get_address_parser_default_options();
    libpostal_address_parser_response_t *parsed = libpostal_parse_address("781 Franklin Ave Crown Heights Brooklyn NYC NY 11216 USA", options);

    for (size_t i = 0; i < parsed->num_components; i++) {
        printf("%s: %s\n", parsed->labels[i], parsed->components[i]);
    }

    // Free parse result
    libpostal_address_parser_response_destroy(parsed);

    // Teardown (only called once at the end of your program)
    libpostal_teardown();
    libpostal_teardown_parser();
}

Parser labels

The address parser can technically use any string labels that are defined in the training data, but these are the ones currently defined, based on the fields defined in OpenCage's address-formatting library, as well as a few added by libpostal to handle specific patterns:

  • house: venue name e.g. "Brooklyn Academy of Music", and building names e.g. "Empire State Building"
  • category: for category queries like "restaurants", etc.
  • near: phrases like "in", "near", etc. used after a category phrase to help with parsing queries like "restaurants in Brooklyn"
  • house_number: usually refers to the external (street-facing) building number. In some countries this may be a compount, hyphenated number which also includes an apartment number, or a block number (a la Japan), but libpostal will just call it the house_number for simplicity.
  • road: street name(s)
  • unit: an apartment, unit, office, lot, or other secondary unit designator
  • level: expressions indicating a floor number e.g. "3rd Floor", "Ground Floor", etc.
  • staircase: numbered/lettered staircase
  • entrance: numbered/lettered entrance
  • po_box: post office box: typically found in non-physical (mail-only) addresses
  • postcode: postal codes used for mail sorting
  • suburb: usually an unofficial neighborhood name like "Harlem", "South Bronx", or "Crown Heights"
  • city_district: these are usually boroughs or districts within a city that serve some official purpose e.g. "Brooklyn" or "Hackney" or "Bratislava IV"
  • city: any human settlement including cities, towns, villages, hamlets, localities, etc.
  • island: named islands e.g. "Maui"
  • state_district: usually a second-level administrative division or county.
  • state: a first-level administrative division. Scotland, Northern Ireland, Wales, and England in the UK are mapped to "state" as well (convention used in OSM, GeoPlanet, etc.)
  • country_region: informal subdivision of a country without any political status
  • country: sovereign nations and their dependent territories, anything with an ISO-3166 code.
  • world_region: currently only used for appending “West Indies” after the country name, a pattern frequently used in the English-speaking Caribbean e.g. “Jamaica, West Indies”

Examples of normalization

The expand_address API converts messy real-world addresses into normalized equivalents suitable for search indexing, hashing, etc.

Here's an interactive example using the Python binding:

expand

libpostal contains an OSM-trained language classifier to detect which language(s) are used in a given address so it can apply the appropriate normalizations. The only input needed is the raw address string. Here's a short list of some less straightforward normalizations in various languages.

Input Output (may be multiple in libpostal)
One-hundred twenty E 96th St 120 east 96th street
C/ Ocho, P.I. 4 calle 8 polígono industrial 4
V XX Settembre, 20 via 20 settembre 20
Quatre vingt douze R. de l'Église 92 rue de l eglise
ул Каретный Ряд, д 4, строение 7 улица каретныи ряд дом 4 строение 7
ул Каретный Ряд, д 4, строение 7 ulitsa karetnyy ryad dom 4 stroyeniye 7
Marktstraße 14 markt strasse 14

libpostal currently supports these types of normalizations in 60+ languages, and you can add more (without having to write any C).

For further reading and some bizarre address edge-cases, see: Falsehoods Programmers Believe About Addresses.

Usage (normalization)

Here's an example using the Python bindings for succinctness (most of the higher-level language bindings are similar):

from postal.expand import expand_address
expansions = expand_address('Quatre-vingt-douze Ave des Champs-Élysées')

assert '92 avenue des champs-elysees' in set(expansions)

The C API equivalent is a few more lines, but still fairly simple:

#include <stdio.h>
#include <stdlib.h>
#include <libpostal/libpostal.h>

int main(int argc, char **argv) {
    // Setup (only called once at the beginning of your program)
    if (!libpostal_setup() || !libpostal_setup_language_classifier()) {
        exit(EXIT_FAILURE);
    }

    size_t num_expansions;
    libpostal_normalize_options_t options = libpostal_get_default_options();
    char **expansions = libpostal_expand_address("Quatre-vingt-douze Ave des Champs-Élysées", options, &num_expansions);

    for (size_t i = 0; i < num_expansions; i++) {
        printf("%s\n", expansions[i]);
    }

    // Free expansions
    libpostal_expansion_array_destroy(expansions, num_expansions);

    // Teardown (only called once at the end of your program)
    libpostal_teardown();
    libpostal_teardown_language_classifier();
}

Command-line usage (expand)

After building libpostal:

cd src/

./libpostal "Quatre vingt douze Ave des Champs-Élysées"

If you have a text file or stream with one address per line, the command-line interface also accepts input from stdin:

cat some_file | ./libpostal --json

Command-line usage (parser)

After building libpostal:

cd src/

./address_parser

address_parser is an interactive shell. Just type addresses and libpostal will parse them and print the result.

Bindings

Libpostal is designed to be used by higher-level languages. If you don't see your language of choice, or if you're writing a language binding, please let us know!

Officially supported language bindings

Unofficial language bindings

Database extensions

Unofficial REST API

Libpostal REST Docker

Libpostal ZeroMQ Docker

Tests

libpostal uses greatest for automated testing. To run the tests, use:

make check

Adding test cases is easy, even if your C is rusty/non-existent, and we'd love contributions. We use mostly functional tests checking string input against string output.

libpostal also gets periodically battle-tested on millions of addresses from OSM (clean) as well as anonymized queries from a production geocoder (not so clean). During this process we use valgrind to check for memory leaks and other errors.

Data files

libpostal needs to download some data files from S3. The basic files are on-disk representations of the data structures necessary to perform expansion. For address parsing, since model training takes a few days, we publish the fully trained model to S3 and will update it automatically as new addresses get added to OSM, OpenAddresses, etc. Same goes for the language classifier model.

Data files are automatically downloaded when you run make. To check for and download any new data files, you can either run make, or run:

libpostal_data download all $YOUR_DATA_DIR/libpostal

And replace $YOUR_DATA_DIR with whatever you passed to configure during install.

Language dictionaries

libpostal contains a number of per-language dictionaries that influence expansion, the language classifier, and the parser. To explore the dictionaries or contribute abbreviations/phrases in your language, see resources/dictionaries.

Training data

In machine learning, large amounts of training data are often essential for getting good results. Many open-source machine learning projects either release only the model code (results reproducible if and only if you're Google), or a pre-baked model where the training conditions are unknown.

Libpostal is a bit different because it's trained on open data that's available to everyone, so we've released the entire training pipeline (the geodata package in this repo), as well as the resulting training data itself on the Internet Archive. It's over 100GB unzipped.

Training data are stored on archive.org by the date they were created. There's also a file stored in the main directory of this repo called current_parser_training_set which stores the date of the most recently created training set. To always point to the latest data, try something like: latest=$(cat current_parser_training_set) and use that variable in place of the date.

Parser training sets

All files can be found at https://archive.org/download/libpostal-parser-training-data-YYYYMMDD/$FILE as gzip'd tab-separated values (TSV) files formatted like:language\tcountry\taddress.

  • formatted_addresses_tagged.random.tsv.gz (ODBL): OSM addresses. Apartments, PO boxes, categories, etc. are added primarily to these examples
  • formatted_places_tagged.random.tsv.gz (ODBL): every toponym in OSM (even cities represented as points, etc.), reverse-geocoded to its parent admins, possibly including postal codes if they're listed on the point/polygon. Every place gets a base level of representation and places with higher populations get proportionally more.
  • formatted_ways_tagged.random.tsv.gz (ODBL): every street in OSM (ways with highway=*, with a few conditions), reverse-geocoded to its admins
  • geoplanet_formatted_addresses_tagged.random.tsv.gz (CC-BY): every postal code in Yahoo GeoPlanet (includes almost every postcode in the UK, Canada, etc.) and their parent admins. The GeoPlanet admins have been cleaned up and mapped to libpostal's tagset
  • openaddresses_formatted_addresses_tagged.random.tsv.gz (various licenses, mostly CC-BY): most of the address data sets from OpenAddresses, which in turn come directly from government sources
  • uk_openaddresses_formatted_addresses_tagged.random.tsv.gz (CC-BY): addresses from OpenAddresses UK

If the parser doesn't perform as well as you'd hoped on a particular type of address, the best recourse is to use grep/awk to look through the training data and try to determine if there's some pattern/style of address that's not being captured.

Features

  • Abbreviation expansion: e.g. expanding "rd" => "road" but for almost any language. libpostal supports > 50 languages and it's easy to add new languages or expand the current dictionaries. Ideographic languages (not separated by whitespace e.g. Chinese) are supported, as are Germanic languages where thoroughfare types are concatenated onto the end of the string, and may optionally be separated so Rosenstraße and Rosen Straße are equivalent.

  • International address parsing: Conditional Random Field which parses "123 Main Street New York New York" into {"house_number": 123, "road": "Main Street", "city": "New York", "state": "New York"}. The parser works for a wide variety of countries and languages, not just US/English. The model is trained on over 1 billion addresses and address-like strings, using the templates in the OpenCage address formatting repo to construct formatted, tagged traning examples for every inhabited country in the world. Many types of normalizations are performed to make the training data resemble real messy geocoder input as closely as possible.

  • Language classification: multinomial logistic regression trained (using the FTRL-Proximal method to induce sparsity) on all of OpenStreetMap ways, addr:* tags, toponyms and formatted addresses. Labels are derived using point-in-polygon tests for both OSM countries and official/regional languages for countries and admin 1 boundaries respectively. So, for example, Spanish is the default language in Spain but in different regions e.g. Catalunya, Galicia, the Basque region, the respective regional languages are the default. Dictionary-based disambiguation is employed in cases where the regional language is non-default e.g. Welsh, Breton, Occitan. The dictionaries are also used to abbreviate canonical phrases like "Calle" => "C/" (performed on both the language classifier and the address parser training sets)

  • Numeric expression parsing ("twenty first" => 21st, "quatre-vingt-douze" => 92, again using data provided in CLDR), supports > 30 languages. Handles languages with concatenated expressions e.g. milleottocento => 1800. Optionally normalizes Roman numerals regardless of the language (IX => 9) which occur in the names of many monarchs, popes, etc.

  • Fast, accurate tokenization/lexing: clocked at > 1M tokens / sec, implements the TR-29 spec for UTF8 word segmentation, tokenizes East Asian languages chracter by character instead of on whitespace.

  • UTF8 normalization: optionally decompose UTF8 to NFD normalization form, strips accent marks e.g. à => a and/or applies Latin-ASCII transliteration.

  • Transliteration: e.g. улица => ulica or ulitsa. Uses all CLDR transforms, the exact same source data as used by ICU, though libpostal doesn't require pulling in all of ICU (might conflict with your system's version). Note: some languages, particularly Hebrew, Arabic and Thai may not include vowels and thus will not often match a transliteration done by a human. It may be possible to implement statistical transliterators for some of these languages.

  • Script detection: Detects which script a given string uses (can be multiple e.g. a free-form Hong Kong or Macau address may use both Han and Latin scripts in the same address). In transliteration we can use all applicable transliterators for a given Unicode script (Greek can for instance be transliterated with Greek-Latin, Greek-Latin-BGN and Greek-Latin-UNGEGN).

Non-goals

  • Verifying that a location is a valid address
  • Actually geocoding addresses to a lat/lon (that requires a database/search index)

Raison d'être

libpostal was originally created as part of the OpenVenues project to solve the problem of venue deduping. In OpenVenues, we have a data set of millions of places derived from terabytes of web pages from the Common Crawl. The Common Crawl is published monthly, and so even merging the results of two crawls produces significant duplicates.

Deduping is a relatively well-studied field, and for text documents like web pages, academic papers, etc. there exist pretty decent approximate similarity methods such as MinHash.

However, for physical addresses, the frequent use of conventional abbreviations such as Road == Rd, California == CA, or New York City == NYC complicates matters a bit. Even using a technique like MinHash, which is well suited for approximate matches and is equivalent to the Jaccard similarity of two sets, we have to work with very short texts and it's often the case that two equivalent addresses, one abbreviated and one fully specified, will not match very closely in terms of n-gram set overlap. In non-Latin scripts, say a Russian address and its transliterated equivalent, it's conceivable that two addresses referring to the same place may not match even a single character.

As a motivating example, consider the following two equivalent ways to write a particular Manhattan street address with varying conventions and degrees of verbosity:

  • 30 W 26th St Fl #7
  • 30 West Twenty-sixth Street Floor Number 7

Obviously '30 W 26th St Fl #7 != '30 West Twenty-sixth Street Floor Number 7' in a string comparison sense, but a human can grok that these two addresses refer to the same physical location.

libpostal aims to create normalized geographic strings, parsed into components, such that we can more effectively reason about how well two addresses actually match and make automated server-side decisions about dupes.

So it's not a geocoder?

If the above sounds a lot like geocoding, that's because it is in a way, only in the OpenVenues case, we have to geocode without a UI or a user to select the correct address in an autocomplete dropdown. Given a database of source addresses such as OpenAddresses or OpenStreetMap (or all of the above), libpostal can be used to implement things like address deduping and server-side batch geocoding in settings like MapReduce or stream processing.

Now, instead of trying to bake address-specific conventions into traditional document search engines like Elasticsearch using giant synonyms files, scripting, custom analyzers, tokenizers, and the like, geocoding can look like this:

  1. Run the addresses in your database through libpostal's expand_address
  2. Store the normalized string(s) in your favorite search engine, DB, hashtable, etc.
  3. Run your user queries or fresh imports through libpostal and search the existing database using those strings

In this way, libpostal can perform fuzzy address matching in constant time relative to the size of the data set.

Why C?

libpostal is written in C for three reasons (in order of importance):

  1. Portability/ubiquity: libpostal targets higher-level languages that people actually use day-to-day: Python, Go, Ruby, NodeJS, etc. The beauty of C is that just about any programming language can bind to it and C compilers are everywhere, so pick your favorite, write a binding, and you can use libpostal directly in your application without having to stand up a separate server. We support Mac/Linux (Windows is not a priority but happy to accept patches), have a standard autotools build and an endianness-agnostic file format for the data files. The Python bindings, are maintained as part of this repo since they're needed to construct the training data.

  2. Memory-efficiency: libpostal is designed to run in a MapReduce setting where we may be limited to < 1GB of RAM per process depending on the machine configuration. As much as possible libpostal uses contiguous arrays, tries (built on contiguous arrays), bloom filters and compressed sparse matrices to keep memory usage low. It's possible to use libpostal on a mobile device with models trained on a single country or a handful of countries.

  3. Performance: this is last on the list for a reason. Most of the optimizations in libpostal are for memory usage rather than performance. libpostal is quite fast given the amount of work it does. It can process 10-30k addresses / second in a single thread/process on the platforms we've tested (that means processing every address in OSM planet in a little over an hour). Check out the simple benchmark program to test on your environment and various types of input. In the MapReduce setting, per-core performance isn't as important because everything's being done in parallel, but there are some streaming ingestion applications at Mapzen where this needs to run in-process.

C conventions

libpostal is written in modern, legible, C99 and uses the following conventions:

  • Roughly object-oriented, as much as allowed by C
  • Almost no pointer-based data structures, arrays all the way down
  • Uses dynamic character arrays (inspired by sds) for safer string handling
  • Confines almost all mallocs to name_new and all frees to name_destroy
  • Efficient existing implementations for simple things like hashtables
  • Generic containers (via klib) whenever possible
  • Data structrues take advantage of sparsity as much as possible
  • Efficient double-array trie implementation for most string dictionaries
  • Cross-platform as much as possible, particularly for *nix

Preprocessing (Python)

The geodata Python package in the libpostal repo contains the pipeline for preprocessing the various geo data sets and building training data for the C models to use. This package shouldn't be needed for most users, but for those interested in generating new types of addresses or improving libpostal's training data, this is where to look.

Address parser accuracy

On held-out test data (meaning labeled parses that the model has not seen before), the address parser achieves 99.45% full parse accuracy.

For some tasks like named entity recognition it's preferable to use something like an F1 score or variants, mostly because there's a class bias problem (most words are non-entities, and a system that simply predicted non-entity for every token would actually do fairly well in terms of accuracy). That is not the case for address parsing. Every token has a label and there are millions of examples of each class in the training data, so accuracy is preferable as it's a clean, simple and intuitive measure of performance.

Here we use full parse accuracy, meaning we only give the parser one "point" in the numerator if it gets every single token in the address correct. That should be a better measure than simply looking at whether each token was correct.

Improving the address parser

Though the current parser works quite well for most standard addresses, there is still room for improvement, particularly in making sure the training data we use is as close as possible to addresses in the wild. There are two primary ways the address parser can be improved even further (in order of difficulty):

  1. Contribute addresses to OSM. Anything with an addr:housenumber tag will be incorporated automatically into the parser next time it's trained.
  2. If the address parser isn't working well for a particular country, language or style of address, chances are that some name variations or places being missed/mislabeled during training data creation. Sometimes the fix is to update the formats at: https://github.com/OpenCageData/address-formatting, and in many other cases there are relatively simple tweaks we can make when creating the training data that will ensure the model is trained to handle your use case without you having to do any manual data entry. If you see a pattern of obviously bad address parses, the best thing to do is post an issue to Github.

Contributing

Bug reports, issues and pull requests are welcome. Please read the contributing guide before submitting your issue, bug report, or pull request.

Submit issues at: https://github.com/openvenues/libpostal/issues.

Shoutouts

Special thanks to @BenK10 for the initial Windows build and @AeroXuk for integrating it seamlessly into the project and setting up an Appveyor build.

License

The software is available as open source under the terms of the MIT License.

Owner
openvenues
An open source project sponsored by Mapzen.
openvenues
Comments
  • Windows support via AppVeyor

    Windows support via AppVeyor

    This pull request is based on the 'BenK10/libpostal_windows' patch and expanded apon.

    I have gone through all the files reviewing the changes and build scripts and making minor changes that will allow this library to be built on Windows via MSYS2 & MingW64. I have setup AppVeyor which from other Issue logs I saw was a requirement for the libpostal dev team to take on Windows support.

    AppVeyor has been configured to build and package the resulting binary into a .zip file along with the relevant linking files libpostal.dll, libpostal.lib, libpostal.exp & libpostal.def.

    The package built by AppVeyor can be uploaded as a Release or linked to via the following URL: https://ci.appveyor.com/api/projects/<account>/libpostal/artifacts/libpostal.zip

    The changes I have made build successfully on Travis CI & AppVeyor.

    The only program not working on this build is the example console app address_parser. This is due to not having a termios.h equivalent. So this step of the build is commented out for the Windows build (it is still compiled for the linux build).

  • Suite/Apartment parsing is not correct

    Suite/Apartment parsing is not correct

    @daguar and I have been experimenting with @straup’s new libpostal API and finding some weird stuff with unit numbers in U.S. addresses. In most cases, libpostal misinterprets unit numbers as house numbers, and groups terms like "suite" with the road name.

    Here are some odd examples:

    • https://libpostal.mapzen.com/parse?address=123+main+street+apt+456+oakland+ca+94789&format=keys
    {
        "city": [
            "oakland"
        ],
        "house_number": [
            "123",
            "456"
        ],
        "postcode": [
            "94789"
        ],
        "road": [
            "main street apt"
        ],
        "state": [
            "ca"
        ]
    }
    
    • https://libpostal.mapzen.com/parse?address=123+main+street+suite+456+oakland+ca+94789&format=keys
    {
        "city": [
            "oakland"
        ],
        "house_number": [
            "123"
        ],
        "postcode": [
            "94789"
        ],
        "road": [
            "main street suite 456"
        ],
        "state": [
            "ca"
        ]
    }
    
    • https://libpostal.mapzen.com/parse?address=123+main+street+%23456+oakland+ca+94789&format=keys
    {
        "city": [
            "oakland"
        ],
        "house_number": [
            "123"
        ],
        "postcode": [
            "94789"
        ],
        "road": [
            "main street # 456"
        ],
        "state": [
            "ca"
        ]
    }
    
  • Parser setup fails with Docker on Windows

    Parser setup fails with Docker on Windows

    Since I'm running a Windows machine, I've been using libpostal inside a Docker container with a Debian image for the last few weeks. This worked fine until I decided to rebuild the image with the latest libpostal release on Friday April 7th, 2017. Now when I run the address parser command line tool, I get the following error: could not find parser model file of known type at address_parser_load (address_parser.c:208) errno: no such file or directory

    it does not say which file is missing.

  • Installation error on RedHat 7.3

    Installation error on RedHat 7.3

    I'm working on Red Hat EL 7.3, trying to install libpostal. when I run the bootstrap.sh, I get:

    libtoolize: putting auxiliary files in '.'. libtoolize: copying file './ltmain.sh' libtoolize: putting macros in AC_CONFIG_MACRO_DIRS, 'm4'. libtoolize: copying file 'm4/libtool.m4' libtoolize: copying file 'm4/ltoptions.m4' libtoolize: copying file 'm4/ltsugar.m4' libtoolize: copying file 'm4/ltversion.m4' libtoolize: copying file 'm4/lt~obsolete.m4' libtoolize: Consider adding '-I m4' to ACLOCAL_AMFLAGS in Makefile.am. configure.ac:12: installing './missing' src/Makefile.am: installing './depcomp'

    then I run configure and then I get the following error when I run make:

    libpostal data file up to date make[1]: *** [all-local] Error 7 make[1]: Leaving directory `[my_dir]/libpostal/src' make: *** [install-recursive] Error 1

    my package versions: autoconf: 2.69  automake: 1.13 libtool: 2.4.2  pkgconfig: 0.27

    (when I do yum --showduplicates list, there is no pkgconfig 0.29 or automake 1.15)

    Any idea what's going wrong ?

  • libpostal installation failed

    libpostal installation failed

    Hi AI,

    I just tried to install latest libpostal and run address_parser, the installation went through but address_parser failed to start. Here are the details:

    Install libpostal on my Mac OS. I tried two Mac OS and both have same problem.

    git clone https://github.com/openvenues/libpostal cd libpostal ./bootstrap.sh ./configure --datadir=$PWD make (tried make -j4 on another Mac, same problem, guess -j4 doesn't matter much) sudo make install

    cd libpostal/src ./address_parser Loading models... ERR Error loading transliteration module, dir=(null) at libpostal_setup_datadir (libpostal.c:266) errno: No such file or directory

    I also tried one linux env and not able to start jpostal's address parser. I didn't try libpostal/src/address_parser on that linux machine because my account doesn't have necessary privilege, but I guess the root cause is same. sudo ldconfig

    Could you help take a look?

    Thanks a lot!

    Tracy

  • memory leak: expand_address hangs indefinitely on CYRILLIC SMALL LETTER YERU

    memory leak: expand_address hangs indefinitely on CYRILLIC SMALL LETTER YERU

    Hi Al!

    Using the node-postal npm module, the following command hangs indefinitely:

    $ node
    > var libpostal = require('node-postal');
    undefined
    
    > libpostal.expand.expand_address('улица 40 лет Победы');
    

    I'm not sure if it's a bug in libpostal core or in the node wrapper.

    Based off master branch, current at time of writing.

  • What are the possible labels?

    What are the possible labels?

    As someone trying to fit the output into a tabular datastructure it'd be good to know what the range of labels for parsed addresses are (I've tried to dig through the code, but...there's kind of a lot of it!)

  • Error loading geodb module

    Error loading geodb module

    I just installed on a Debian system, following the step-by-step instructions in the readme. Then I tried running the parser from the command line, and got this error:

    # ./address_parser
    Loading models...
    ERR   Error loading geodb module
       at libpostal_setup_parser (libpostal.c:1071) errno: None
    

    I made sure the data directory is world-readable. What am I missing?

  • Doesn't appear to handle PO Box numbers.

    Doesn't appear to handle PO Box numbers.

    postal.parser.parse_address('PO Box 1, Seattle, WA 98103');
    [ { value: 'po', component: 'house_number' },
      { value: 'box', component: 'road' },
      { value: '1', component: 'house_number' },
      { value: 'seattle', component: 'city' },
      { value: 'wa', component: 'state' },
      { value: '98103', component: 'postcode' } ]
    
  • Configurable data library directory to enable hadoop deployment

    Configurable data library directory to enable hadoop deployment

    Hi there,

    We are trying to use LibPostal (+jpostal) as part of a wider data processing pipeline based in Spark running on YARN. We have got this working on our development environment by performing a lib postal install on each of the hadoop nodes and are able to parse addresses in parallel across the hadoop cluster.

    Unfortunately we have hit a bit of an issue when trying to deploy this within our customer environments because they do not allow for the install to occur on the individual nodes on the cluster.

    From an installation perspective there are four components as we understand it:

    1. LibPostal Data Library (all the reference data load to the configured datadir).
    2. The libpostal C libraries which need to be on the LD_LIBRARY_PATH
    3. JPostal’s JNI C libraries which need to be on the java.library.path
    4. JPostal’s java library (jpostal.jar)

    The main issue we are having is that the location of the data library / directory is hard coded into libpostal C library at the time of compile, which makes the libraries non portable.

    Question: Would it be possible to make the data library/directory configurable either as an argument in the call to the library or as an environment variable? Obviously for backward compatibility, libpostal could use the "defaults" (set at compile time) but if the relevant argument / environment variable was set, could use this.

    Thanks, Jamie

  • Error loading address parser module errno:Not enough space windows

    Error loading address parser module errno:Not enough space windows

    I compiled libpostal library and followed the instruction for windows and everything looks good and everything looks download correctly the data file that contains address_expansions, address_parser, ..etc is downloaded completely with size of 1.84 GB then i use the sample code for libpostal, after i link the library in QT IDE using

    LIBS += D:\cook\libpostal\libpostal\src\.libs\libpostal.a
    LIBS += D:\cook\libpostal\libpostal\src\.libs\libpostal.dll.a
    LIBS += D:\cook\libpostal\libpostal\src\.libs\libscanner.a
    INCLUDEPATH += D:\cook\libpostal\libpostal\src\
    

    then I use this code to set the datadir and parser datadir then simple example of parse address

    libpostal_setup_datadir("D:\\cook\\libpostal\\Data\\libpostal");
    libpostal_setup_parser_datadir("D:\\cook\\libpostal\\Data\\libpostal");
    
    
    // Setup (only called once at the beginning of your program)
    if (!libpostal_setup() || !libpostal_setup_parser()) {
        exit(EXIT_FAILURE);
    }
    
    libpostal_address_parser_options_t options = libpostal_get_address_parser_default_options();
    libpostal_address_parser_response_t *parsed = libpostal_parse_address("781 Franklin Ave Crown Heights Brooklyn NYC NY 11216 USA", options);
    
    for (size_t i = 0; i < parsed->num_components; i++) {
        printf("%s: %s\n", parsed->labels[i], parsed->components[i]);
    }
    
    // Free parse result
    libpostal_address_parser_response_destroy(parsed);
    
    // Teardown (only called once at the end of your program)
    libpostal_teardown();
    libpostal_teardown_parser();
    
    return 0;
    
    

    but I got this error

    ERR   Error loading address parser module, dir=D:\cook\libpostal\Data\libpostal\address_parser
     at libpostal_setup_parser_datadir (libpostal.c:434) errno:Not enough space
    ERR   parser is not setup, call libpostal_setup_address_parser()
     at address_parser_parse (address_parser.c:1666) errno:Not enough space
    ERR   Parser returned NULL
     at libpostal_parse_address (libpostal.c:267) errno:Not enough space
    
    

    so what i did wrong, I have for sure space on my disk more than 100GB and i still got this error.

  • Difficulty Installing libpostal

    Difficulty Installing libpostal

    Hello!

    I am having a lot of trouble following the instructions and installing the R libpostal library.

    I was wondering - perhaps in the future, this package could be made into a "traditional R package" and installed directly (like most other R packages)?

    Thanks!

  • parse_address not working well for Japanese addresses

    parse_address not working well for Japanese addresses

    Hi!

    I was checking out libpostal, and have a question: I read that this library supports Japanese addresses parsing, however, when I tried, it doesn't seem working well. So I would like to get some feedback from the awesome contributors (tried for other countries, and it works really great!)


    My country is

    US, but I'm using it for parsing Japanese addresses


    Here's how I'm using libpostal

    To help extract address from small business owner's website


    Here's what I did

    text = '〒100-8994 東京都千代田区丸ノ内2-7-2' parse_address(text)


    Here's what I got

    [('〒100-8994', 'postcode'), ('東', 'city'), ('京都千代田', 'city_district'), ('区', 'city'), ('丸ノ内', 'road'), ('2-7-2', 'house_number')]


    Here's what I was expecting

    postcode is correct, but "東京都" (means Tokyo Capital) is supposed to be city, "千代田区" is supposed to be city district

    Here are a few other examples

    Example 1 input: text = '〒550-0002 大阪府大阪市西区江戸堀1丁目18番21号' parse_address(text)

    output: [('〒550-0002', 'postcode'), ('大', 'state'), ('阪', 'city'), ('府大阪市西', 'city_district'), ('区', 'city'), ('江戸堀', 'house'), ('1丁目', 'suburb'), ('18番', 'house_number'), ('21号', 'city_district')]

    expected/correct parsing: 〒550-0002 大阪府 / 大阪市 / 西区 / 江戸堀 / 1丁目18番21号

    Example 2 input: text = '〒064-0809 北海道札幌市中央区南9条西3丁目2−5' parse_address(text)

    output: [('〒064-0809', 'postcode'), ('北', 'state'), ('海', 'city'), ('道札幌市中央区南9条西', 'road'), ('3丁目', 'suburb'), ('2-5', 'house_number')]

    expected/correct parsing: 〒064-0809 北海道 / 札幌市 / 中央区 / 南9条西 / 3丁目2−5

    Example 3 input: text = '〒604-8064 京都府京都市中京区骨屋之町560 離れ' parse_address(text)

    output: [('〒604-8064', 'postcode'), ('京', 'state'), ('都', 'city'), ('府京都市中京区', 'city_district'), ('骨屋之町', 'road'), ('560', 'house_number'), ('離れ', 'road')]

    expected/correct parsing: 〒604-8064 京都府 / 京都市 / 中京区 / 骨屋之町 / 560 離れ

    Example 4 input: text = '〒460-0031 愛知県名古屋市中区本丸1−1' parse_address(text)

    output: [('〒460-0031', 'postcode'), ('愛', 'state'), ('知県名古屋市中', 'city'), ('区', 'city_district'), ('本丸', 'suburb'), ('1-1', 'house_number')]

    expected/correct parsing: 〒460-0031 愛知県 / 名古屋市 / 中区 / 本丸 / 1−1


    For parsing issues, please answer "yes" or "no" to all that apply.

    • Does the input address exist in OpenStreetMap?
    • Do all the toponyms exist in OSM (city, state, region names, etc.)?
    • If the address uses a rare/uncommon format, does changing the order of the fields yield the correct result?
    • If the address does not contain city, region, etc., does adding those fields to the input improve the result?
    • If the address contains apartment/floor/sub-building information or uncommon formatting, does removing that help? Is there any minimum form of the address that gets the right parse?

    Here's what I think could be improved

  • Enabling SSE creates memory write access violations

    Enabling SSE creates memory write access violations

    There are two main issues:

    The remez9_0_log2_sse function assumes that the buffer it is handed has a size of multiples of 4 doubles. This isn't assured anywhere and make check will cause an access violation in the crf_context test because it allocates a buffer of 9.

    The posix_memalign call "almost" handles this by aligning to 16 (2 doubles) as the allocated buffer will always be multiples of the alignment. Changing the alignment from 16 to 32 resolves this problem.

    There is no such thing as a realloc for aligned memory, but vector.h tries to implement one. It is undefined whether realloc on a posix_memalign allocation even works... though from searching google it sounds like it does. But the problem is realloc doesn't take into account that the size needs to be a multiple of the alignment. So when the unit tests asks for 72, it gets 72 and the call to remez9_0_log2_sse gives an access violation.

    The safe thing here would be to not use realloc. But then you have the issue that the _aligned_realloc function doesn't know the existing size of the buffer in order to do the copy. So you have to align the size to realloc yourself and hope the C library doesn't corrupt the heap.

    There is something else going on too that I haven't figured out but my recommendation at the moment is to simply disable SSE by default.

  • make: *** No rule to make target 'install'.  Stop.

    make: *** No rule to make target 'install'. Stop.

    Hi hi team.when trying to install libpostal,it failed and told me "make: *** No rule to make target 'install'. Stop." I am installing in dockefile

    RUN git clone https://github.com/openvenues/libpostal &&
    cd libpostal &&
    make distclean &&
    /bin/bash bootstrap.sh &&
    ./configure --disable-data-download --datadir=/data/ &&
    make -j4 &
    make install &&
    libpostal_data download all /data/ &&
    ldconfig

  • Option to disable street name expansion for near dupes

    Option to disable street name expansion for near dupes

    We found that for near dupes the street name root expansion generated a lot of extra hashes but didn't give us a lot of benefits in terms of blocking, so we ended up disabling it.

  • 'make check' fails after the recent update on 2022/07/11

    'make check' fails after the recent update on 2022/07/11

    Hi!

    We were building the libpostal in a docker image and it suddenly started failing on make check step of the following Dockerfile after the recent change on 2022/07/11 This PR: https://github.com/openvenues/libpostal/pull/578/files


    My country is

    Canada


    Here's how I'm using libpostal

    Geocoding, address parsing.


    Here's what I did

    FROM golang:1.12
    
    # libpostal apt dependencies
    # note: this is done in one command in order to keep down the size of intermediate containers
    RUN apt-get update && \
        apt-get install -y autoconf automake libtool pkg-config python && \
        rm -rf /var/lib/apt/lists/*
    
    # install libpostal
    RUN git clone https://github.com/openvenues/libpostal /code/libpostal
    WORKDIR /code/libpostal
    # RUN git checkout a97717f2b9f8fba03d25442f2bd88c15e86ec81b <--2022/04/18 ver. It is working.
    RUN ./bootstrap.sh
    
    RUN ./configure --datadir=/usr/share/libpostal
    RUN make -j4
    # it failed at this stage -------------------
    RUN make check
    RUN make install
    RUN ldconfig
    

    Here's what I got

    [email protected]:/code/libpostal# make check
    Making check in src
    make[1]: Entering directory '/code/libpostal/src'
    ./libpostal_data download all /usr/share/libpostal/libpostal
    Checking for new libpostal data file...
    libpostal data file up to date
    Checking for new libpostal parser data file...
    libpostal parser data file up to date
    Checking for new libpostal language classifier data file...
    libpostal language classifier data file up to date
    make[1]: Leaving directory '/code/libpostal/src'
    Making check in test
    make[1]: Entering directory '/code/libpostal/test'
    make  check-TESTS
    make[2]: Entering directory '/code/libpostal/test'
    make[3]: Entering directory '/code/libpostal/test'
    ../test-driver: line 107:   350 Aborted                 (core dumped) "[email protected]" > $log_file 2>&1
    FAIL: test_libpostal
    ============================================================================
    Testsuite summary for libpostal 1.1.0
    ============================================================================
    # TOTAL: 1
    # PASS:  0
    # SKIP:  0
    # XFAIL: 0
    # FAIL:  1
    # XPASS: 0
    # ERROR: 0
    ============================================================================
    See test/test-suite.log
    ============================================================================
    make[3]: *** [Makefile:1001: test-suite.log] Error 1
    make[3]: Leaving directory '/code/libpostal/test'
    make[2]: *** [Makefile:1109: check-TESTS] Error 2
    make[2]: Leaving directory '/code/libpostal/test'
    make[1]: *** [Makefile:1182: check-am] Error 2
    make[1]: Leaving directory '/code/libpostal/test'
    make: *** [Makefile:459: check-recursive] Error 1
    [email protected]:/code/libpostal# 
    

    Here's what I was expecting


    For parsing issues, please answer "yes" or "no" to all that apply.

    • Does the input address exist in OpenStreetMap?
    • Do all the toponyms exist in OSM (city, state, region names, etc.)?
    • If the address uses a rare/uncommon format, does changing the order of the fields yield the correct result?
    • If the address does not contain city, region, etc., does adding those fields to the input improve the result?
    • If the address contains apartment/floor/sub-building information or uncommon formatting, does removing that help? Is there any minimum form of the address that gets the right parse?

    Here's what I think could be improved

A wrapper around std::variant with some helper functions

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