country-named-entity-recognition 1.0.2

Finds countries in a string

🌐 fastdatascience.com

Country named entity recognition

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.

New! In 2025 we have added support for spaCy

Requirements

Python 3.9 and above

pycountry 22.1.10 and above

Installation

pip install country-named-entity-recognition

Usage examples

Example 1

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'>)]

Example 2

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)

Example 3

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)

Adding custom variants

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")

Using the library with spaCy

If you are already using spaCy in your project, you'll be pleased to see that you can pass a spaCy Doc object into the tool:

import spacy
from country_named_entity_recognition.country_finder_spacy import find_countries_in_spacy_doc
nlp = spacy.blank("en")
doc = nlp("I went to the USA")
country_matches = find_countries_in_spacy_doc(nlp, doc)
print (country_matches)

Raising issues

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.

Who to contact

Thomas Wood at https://fastdatascience.com

How to cite Country Named Entity Recognition?

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. Zenodo, 5 Sept. 2025, https://doi.org/10.5281/zenodo.17062716.

A BibTeX entry for LaTeX users is

@unpublished{countrynamedentityrecognition,
    AUTHOR = {Wood, T.A.},
    TITLE  = {Country Named Entity Recognition (Computer software), Version 1.0.2},
    YEAR   = {2025},
    doi    = {10.5281/zenodo.17062716},
    url = {https://fastdatascience.com/natural-language-processing/country-named-entity-recognition/}
}

Case studies of the Country Named Entity Recognition Library

People and organisations around the world have been using the library and have cited us.

The sixth wave of mass species extinction...

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.

The UN's Sustainable Development Goals (SDGs)

Christoph Funk and his colleagues at Justus-Liebig-Universität Gießen (Justus Liebig University Giessen) in Germany used country-named-entity-recognition for their meta-analysis of articles related to Sustainable Development Goals in 2023:

• Funk, Christoph and Tönjes, Elena and Teuber, Ramona and Breuer, Lutz, Reading Between the Lines: The Intersection of Research Attention and Sustainable Development Goals (May 31, 2023). Available at SSRN: https://ssrn.com/abstract=4465055 or http://dx.doi.org/10.2139/ssrn.4465055

The European Commission: detecting terrorism and extremism

Francesco Bosso and his team at the European Commission wrote a report investigating NLP for location detection with a focus on the JRC Terrorism and Extremism Database.

• Bosso, Francesco, et al. Use of Large Language Models for location detection on the example of the terrorism and extremism event database., JRC Technical Report, European Commission (2023).

Labelling radical content online

Ugochukwu Etudo and Victoria Y. Yoon at Virginia Commonwealth University used the tool in their analysis of radical content online:

• Ugochukwu Etudo, Victoria Y. Yoon (2023) Ontology-Based Information Extraction for Labeling Radical Online Content Using Distant Supervision. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1223

Analysing text to assess indicators in Sustainable Development Goals

Elena Tönjes used the library to assess sustainability indicators in her PhD thesis:

• Tönjes, Elena. A Text-based Approach to Sustainability Indicators. Diss. Justus-Liebig-University Giessen, 2024.

Other named entity recognition tools

• Medical and clinical named entity recognition Python library