Mordecai requires a running Elasticsearch service with Geonames in it. See "Installation" below for instructions.

1. Mordecai is on PyPI and can be installed for Python 3 with pip:

Note: It's strongly recommended that you run Mordecai in a virtual environment. The libraries that Mordecai depends on are not always the most recent versions and using a virtual environment prevents libraries from being downgraded or running into other issues:

2. You should then download the required spaCy NLP model:

3. In order to work, Mordecai needs access to a Geonames gazetteer running in Elasticsearch. The easiest way to set it up is by running the following commands (you must have Docker installed first).

See the es-geonames for the code used to produce this index.

To update the index, simply shut down the old container, re-download the index from s3, and restart the container with the new index.

If you use this software in academic work, please cite as

Mordecai takes in unstructured text and returns structured geographic information extracted from it.

• It uses spaCy's named entity recognition to extract placenames from the text.

• It uses the geonames gazetteer in an Elasticsearch index (with some custom logic) to find the potential coordinates of extracted place names.

• It uses neural networks implemented in Keras and trained on new annotated English-language data labeled with Prodigy to infer the correct country and correct gazetteer entries for each placename.

The training data for the two models includes copyrighted text so cannot be shared freely. Applying Mordecai to non-English language text would require labeling data in the target language and retraining.

When instantiating the Geoparser() module, the following options can be changed:

• es_hosts : List of hosts where the Geonames Elasticsearch service is running. Defaults to ['localhost'], which is where it runs if you're using the default Docker setup described above.
• es_port : What port the Geonames Elasticsearch service is running on. Defaults to 9200, which is where the Docker setup has it
• es_ssl : Whether Elasticsearch requires an SSL connection. Defaults to False.
• es_auth : Optional HTTP auth parameters to use with ES. If provided, it should be a two-tuple of (user, password).
• country_confidence : Set the country model confidence below which no geolocation will be returned. If it's really low, the model's probably wrong and will return weird results. Defaults to 0.6.
• verbose : Return all the features used in the country picking model? Defaults to False.
• threads: whether to use threads to make parallel queries to the Elasticsearch database. Defaults to True, which gives a ~6x speedup.

geoparse is the primary endpoint and the only one that most users will need. Other, mostly internal, methods may be useful in some cases:

• lookup_city takes a city name, country, and (optionally) ADM1/state/governorate and does a rule-based lookup for the city.
• infer_country take a document and attempts to infer the most probable country for each.
• query_geonames and query_geonames_country can be used for performing a search over Geonames in Elasticsearch
• methods with the _feature prefix are internal methods for calculating country picking features from text.

batch_geoparse takes in a list of documents and uses spaCy's nlp.pipe method to process them more efficiently in the NLP step.

Advanced users on large machines can increase the lru_cache parameter from 250 to 1000. This will use more memory but will increase parsing speed.

Mordecai includes unit tests. To run the tests, cd into the mordecai directory and run:

The tests require access to a running Elastic/Geonames service to complete. The tests are currently failing on TravisCI with an unexplained segfault but run fine locally. Mordecai has only been tested with Python 3.

An earlier verion of this software was donated to the Open Event Data Alliance by Caerus Associates. See Releases or the legacy-docker branch for the 2015-2016 and the 2016-2017 production versions of Mordecai.

This work was funded in part by DARPA's XDATA program, the U.S. Army Research Laboratory and the U.S. Army Research Office through the Minerva Initiative under grant number W911NF-13-0332, and the National Science Foundation under award number SBE-SMA-1539302. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA, ARO, Minerva, NSF, or the U.S. government.

Contributions via pull requests are welcome. Please make sure that changes pass the unit tests. Any bugs and problems can be reported on the repo's issues page.