Developed by Fast Data Science, https://fastdatascience.com
Source code at https://github.com/fastdatascience/country_named_entity_recognition
PyPI package: https://pypi.org/project/country-named-entity-recognition/
Python library for finding country names in a string.
Please note this library finds only high confidence countries. A text such as "America" is ambiguous.
It also only finds the English names of these countries. Names in the local language are not supported.
Python 3.9 and above
pycountry 22.1.10
pip install country-named-entity-recognition
from country_named_entity_recognition import find_countries
find_countries("We are expanding in the UK")
outputs a list of tuples.
[(Country(alpha_2='GB', alpha_3='GBR', flag='🇬🇧', name='United Kingdom', numeric='826', official_name='United Kingdom of Great Britain and Northern Ireland'),
<re.Match object; span=(1, 15), match='united kingdom'>)]
The tool's default behaviour assumes countries are correctly capitalised and punctuated:
from country_named_entity_recognition import find_countries
find_countries("I want to visit france.")
will not return anything.
However, if your text comes from social media or another non-moderated source, you might want to allow case-insensitive matching:
from country_named_entity_recognition import find_countries
find_countries("I want to visit france.", is_ignore_case=True)
This illustrates how you can bring context into the tool. If we encounter the string "Georgia", by default it refers to the US state.
from country_named_entity_recognition import find_countries
find_countries("Gladys Knight and the Pips wrote the Midnight Train to Georgia")
will return an empty list.
But what happens if we include a clear contextual clue?
from country_named_entity_recognition import find_countries
find_countries("Salome Zourabichvili is the current president of Georgia.")
returns
[(Country(alpha_2='GE', alpha_3='GEO', flag='🇬🇪', name='Georgia', numeric='268'), <re.Match object; span=(34, 41), match='Georgia'>)]
You can force the latter behaviour:
from country_named_entity_recognition import find_countries
find_countries("I want to visit Georgia.", is_georgia_probably_the_country=True)
If you find that a variant country name is missing, you can add it using the add_custom_variants method.
Let's imagine we want to add Neverneverland as a synonym for the UAE:
from country_named_entity_recognition import find_countries, add_custom_variants
add_custom_variants(["Neverneverland"], "AE")
find_countries("I want to visit Neverneverland")
If you find a problem, you are welcome either to raise an issue at https://github.com/fastdatascience/country_named_entity_recognition/issues or to make a pull request and I will merge it into the project.
Thomas Wood at https://fastdatascience.com
We would be grateful for your taking the consideration to cite us. We would suggest something like the following (depending on your style):
Wood, T.A., Country Named Entity Recognition [Computer software], Version 0.4, accessed at https://fastdatascience.com/country-named-entity-recognition/, Fast Data Science Ltd (2022)
A BibTeX entry for LaTeX users is
@unpublished{countrynamedentityrecognition,
AUTHOR = {Wood, T.A.},
TITLE = {Country Named Entity Recognition (Computer software), Version 0.4},
YEAR = {2023},
Note = {To appear},
url = {https://fastdatascience.com/country-named-entity-recognition/}
}
Alisa Redding at the University of Helsinki used the tool for her Masters thesis on mass species extinction and biodiversity. Redding, Alisa, Animals of the Digital Age : Assessing digital media for public interest and engagement in species threatened by wildlife trade., University of Helsinki, Faculty of Science, 2023.