Building NLP tools for a 20-million speaker language that most AI models ignore. This is the story of bridging computational linguistics with cultural preservation, one morpheme at a time.
When I tell people I'm building machine learning tools for Somali, I usually get one of two reactions: confusion ("Why would you do that?") or excitement ("That's amazing!"). Both reactions miss the deeper point.
This isn't just about technology. It's about preserving the linguistic heritage of 20+ million people who deserve to see their language represented in the digital age.
The Problem with "Universal" AI
Most AI models are trained on English, Chinese, and a handful of other high-resource languages. The assumption is that these models will somehow work for everyone else. They don't.
Try running a Somali text through Google Translate:
**Somali**: "Waxaan ahay injineer software ah oo ku nool Bariga Afrika"
**Google's attempt**: "I am a software engineer living in East Africa"
**Actual meaning**: "I am a software engineer living in East Africa"
Wait, that's actually correct. Let me try something more complex:
**Somali**: "Af-Soomaaliga waa luqad aad u qurux badan oo leh nidaam eray-samaysi oo aad u murugsan"
**Google's attempt**: "Somali is a very beautiful language with a very complex word formation system"
**Actual meaning**: "Somali is a very beautiful language with a very complex morphological system"
Close, but "morphological" and "word formation" aren't the same thing. This might seem like a minor difference, but in linguistics, precision matters.
Why Somali is Computationally Fascinating
Somali belongs to the Cushitic branch of the Afroasiatic language family. It has features that make it both challenging and interesting for computational analysis:
Agglutination
Somali builds complex meanings by stacking morphemes (meaningful units) together:
A single word can carry the meaning of an entire English phrase.
Vowel Harmony
Somali has a complex vowel harmony system where vowels within a word must "agree" with each other:
The vowel in the suffix changes based on the vowels in the root word.
Tonal Variations
Somali uses tone to distinguish meaning:
Most NLP models completely ignore tonal information, making them useless for tone languages.
Building a Morphological Analyzer
I started with the most fundamental task: breaking Somali words into their component morphemes. This is like teaching a computer to understand that "unbreakable" consists of "un-" + "break" + "-able".
The Data Challenge
The first problem was data. There are no large, annotated Somali corpora available for machine learning. I had to build everything from scratch.
I started by digitizing traditional Somali poetry (gabay) and prose. Somali has a rich oral tradition, but most of it exists only in people's memories or on cassette tapes.
# Example of morphological analysis
def analyze_word(word):
"""
Analyze a Somali word into its morphological components
"""
morphemes = []
# Check for prefixes
if word.startswith('ma'): # Negative marker
morphemes.append(('ma', 'NEG'))
word = word[2:]
# Find root
root = extract_root(word)
morphemes.append((root, 'ROOT'))
# Check for suffixes
remaining = word[len(root):]
while remaining:
suffix, tag = identify_suffix(remaining)
morphemes.append((suffix, tag))
remaining = remaining[len(suffix):]
return morphemes
# Example usage
analyze_word('matagayaan')
# Output: [('ma', 'NEG'), ('tag', 'ROOT'), ('ay', '3PL'), ('aan', 'PRES')]
# Meaning: "they don't go"
The Pattern Recognition Challenge
Somali morphology follows complex patterns that aren't immediately obvious. I spent months identifying and cataloging these patterns:
**Verbal conjugation patterns**:
**Nominal declension patterns**:
Each pattern had to be encoded as rules that could handle variations and exceptions.
The Machine Learning Approach
After building a rule-based system, I experimented with machine learning approaches. I trained a sequence-to-sequence model using transformer architecture:
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel
class SomaliMorphAnalyzer(nn.Module):
def __init__(self, vocab_size, hidden_size=512):
super().__init__()
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.encoder = nn.LSTM(hidden_size, hidden_size, batch_first=True)
self.decoder = nn.LSTM(hidden_size, hidden_size, batch_first=True)
self.output_layer = nn.Linear(hidden_size, vocab_size)
def forward(self, input_ids):
# Encode input word
embedded = self.embedding(input_ids)
encoded, hidden = self.encoder(embedded)
# Decode to morphemes
decoded, _ = self.decoder(encoded, hidden)
output = self.output_layer(decoded)
return output
The results were promising but not perfect. The model could handle regular patterns well but struggled with irregular forms and rare words.
Cultural Context Matters
One thing I learned early is that language isn't just about grammar and vocabulary. It's about culture, context, and worldview.
Somali has concepts that don't exist in English:
These aren't just words to be translated. They're cultural concepts that carry deep meaning within Somali society.
The Metaphor Problem
Somali poetry is rich with metaphors that reference pastoral life:
**"Geelu waa la kala guuraa"** literally means "Camels are separated from each other" but metaphorically means "People go their separate ways."
A machine learning model trained only on literal meanings would miss the poetic and cultural significance entirely.
Building for the Community
The most important lesson I learned is that technology for underrepresented languages must be built with the community, not for them.
I partnered with:
Open Source from Day One
All my Somali NLP tools are open source. The data, the models, the code—everything is freely available. This isn't just about transparency; it's about ensuring the community owns their linguistic heritage.
# Install the Somali morphological analyzer
pip install somali-morph
# Use it in your projects
from somali_morph import analyze
result = analyze("buugaggayga")
print(result)
# Output: [('buug', 'book'), ('ag', 'PLURAL'), ('gay', 'POSS.1SG'), ('ga', 'DEF')]
The Ripple Effects
Building NLP tools for Somali has had unexpected consequences:
Educational Impact
Teachers in Somali schools are using our morphological analyzer to help students understand word structure. It's become a teaching tool for grammar and linguistics.
Preservation Efforts
Researchers are using our tools to digitize and analyze historical Somali texts. We're helping preserve centuries of written heritage.
Diaspora Connection
Young Somalis in the diaspora are using our tools to reconnect with their linguistic heritage. Parents are teaching their children Somali grammar using our apps.
The Technical Challenges
Building NLP tools for low-resource languages presents unique challenges:
Limited Training Data
Most machine learning approaches require massive datasets. For Somali, I had to be creative:
Evaluation Metrics
Standard NLP evaluation metrics don't always make sense for morphologically rich languages. I had to develop new metrics that account for partial correctness:
def morphological_accuracy(predicted, actual):
"""
Calculate accuracy that gives partial credit for
partially correct morphological analyses
"""
if len(predicted) != len(actual):
return 0.0
correct_morphemes = 0
total_morphemes = len(actual)
for pred, act in zip(predicted, actual):
if pred[0] == act[0]: # Morpheme matches
if pred[1] == act[1]: # Tag matches
correct_morphemes += 1
else:
correct_morphemes += 0.5 # Partial credit
return correct_morphemes / total_morphemes
Computational Efficiency
Many Somali speakers live in areas with limited internet connectivity. Our tools needed to work offline and on low-powered devices.
I optimized our models for mobile deployment:
# Model quantization for mobile deployment
import torch.quantization as quantization
# Quantize model to reduce size and improve speed
quantized_model = quantization.quantize_dynamic(
model, {nn.Linear}, dtype=torch.qint8
)
# Export for mobile
torch.jit.save(torch.jit.script(quantized_model), 'somali_morph_mobile.pt')
Measuring Success
How do you measure the success of a cultural preservation project? Traditional metrics like accuracy and F1 scores tell only part of the story.
Quantitative Metrics
Qualitative Impact
What's Next
This is just the beginning. We're working on:
Advanced NLP Tasks
Cultural Applications
Technical Infrastructure
The Bigger Picture
Building NLP tools for Somali is about more than just technology. It's about:
Lessons for Other Languages
If you're interested in building NLP tools for your own language, here's what I've learned:
Start Small
Don't try to build everything at once. Start with basic tasks like tokenization and morphological analysis.
Engage the Community
Language communities are your most valuable resource. They provide data, feedback, and validation.
Build for Real Use Cases
Don't build tools in isolation. Find real problems that your community faces and solve them.
Make It Open
Open source your work. Language communities should own their linguistic tools.
Think Long Term
Language preservation is a generational project. Build tools that will last and can be maintained by the community.
The Personal Journey
Working on Somali NLP has been deeply personal. It's connected me to my linguistic heritage in ways I never expected.
I've learned about the poetry of my grandfather's generation, the linguistic innovations of modern Somali writers, and the challenges facing Somali language education around the world.
Every morpheme analyzed, every pattern discovered, every tool built is a small act of cultural preservation. It's my way of ensuring that future generations of Somali speakers will have the digital tools they need to thrive in their mother tongue.
Call to Action
Language diversity is human heritage. Every language that disappears takes with it unique ways of understanding the world.
If you're a developer, consider contributing to NLP tools for underrepresented languages. If you're a speaker of a low-resource language, consider starting a digitization project for your community.
The tools exist. The techniques are proven. What we need now is the will to apply them to all of humanity's languages, not just the dominant few.
---
*The Somali morphological analyzer and related tools are available at [github.com/yussufhersi/somali-nlp](https://github.com/yussufhersi/somali-nlp). If you're interested in contributing or have questions about building NLP tools for your own language, reach out to me at yussuf@hersi.dev.*
*Special thanks to the Somali linguists, poets, and community members who made this work possible. This is your project as much as it is mine.*