NERS-NLP: A Culturally-Aware Natural Language Processing System with Named Entity Recognition and Gender Inference Models

Despite the growing success of Named Entity Recognition (NER) systems and gender inference models in Natural Language Processing (NLP), these tools often underperform when applied to culturally diverse African contexts due to the lack of culturally-representative training data. In this paper, we propose NERS-NLP, a culturally-aware NLP system with Named Entity Recognition and Gender Inference Models. This study introduces a large-scale dataset of over 7 million names of the population of the Democratic Republic of Congo (DRC) annotated with gender and demographic metadata, including geographical distribution. We explore the linguistic and sociocultural features embedded in these names and examine their impact on two key NLP tasks, namely, entity recognition and gender classification. Our approach involves :

• (1) a statistical and feature analysis of Congolese name structures,
• (2) the development of supervised gender prediction models leveraging name components and demographic patterns,
• (3) the integration of the curated name lexicon into NER pipelines to improve recognition accuracy for Congolese entities.

Experiments conducted on custom evaluation sets, including multilingual and code-switched Congolese texts, show that our culturally-aware methods significantly outperform state-of-the-art multilingual baselines. This work demonstrates the importance of culturally grounded resources in reducing bias and improving performance in NLP systems applied to underrepresented regions. Our findings open new directions for inclusive language technologies in African contexts and contribute a valuable resource for future research in regional linguistics, onomastics, and identity-aware artificial intelligence.

Usage

git clone https://github.com/bernard-ng/drc-ners-nlp.git
cd drc-ners-nlp

python3 -m venv .venv
source .venv/bin/activate
cp .env .env.local

pip install -r requirements.txt

Gender Inference

1. Dataset Preparation

python -m processing.gender.prepare

2. Training

Arguments:

Name	Description	Default
--dataset	Path to the dataset file	names_featured.csv
--size	Number of samples to use (None for full dataset)	None
--threshold	Probability threshold for gender classification	0.5
--cv	Number of cross-validation folds	None
--save	Whether to save the trained model	False
--balanced	Whether to balance the dataset	False
--epochs	Number of training epochs	10
--test_size	Proportion of data to use as test set	0.2
--random_state	Random seed for reproducibility	42

Examples:

python -m ners.gender.models.lstm --size 1000000 --save
python -m ners.gender.models.logreg --size 1000000 --save
python -m ners.gender.models.transformer --size 1000000 --save

python -m ners.gender.models.lstm --size 1000000 --balanced --save
python -m ners.gender.models.logreg --size 1000000 --balanced --save
python -m ners.gender.models.transformer --size 1000000 --balanced --save

3. Evaluation

Arguments:

Name	Description	Default
--model	Model type: logreg, lstm, or transformer	(required)
--dataset	Path to the dataset CSV file	names_featured.csv
--size	Number of rows to load from the dataset	None
--balanced	Load balanced dataset	False
--threshold	Probability threshold for classification	0.5

Examples:

python -m ners.gender.eval --dataset names_evaluations.csv --model logreg
python -m ners.gender.eval --dataset names_evaluations.csv --model lstm 
python -m ners.gender.eval --dataset names_evaluations.csv --model transformer

4. Inference

Arguments:

Name	Description	Default
--model	Model type: logreg, lstm, or transformer	(required)
--names	One or more names	(required)
--threshold	Threshold for classification	0.5

Examples:

python -m ners.gender.predict --model logreg --names "Tshisekedi"
python -m ners.gender.predict --model lstm --names "Ilunga Ngandu"
python -m ners.gender.predict --model transformer --names "musenga wa musenga"