The document discusses challenges and opportunities in natural language processing for underserved areas and languages. It outlines how tools like sentiment analysis and word embeddings are not available for many languages and can help applications in healthcare, education, and other areas. The document also presents some recent collaborations aimed at developing NLP resources for languages in Africa through creating custom dictionaries and datasets.

1. NLP: Challenges and Opportunities in
Underserved Areas
Colleen M. Farrelly, Machine Learning Lead

2. Natural Language
Processing
• Many applications of text data
• Customer feedback
• Legal documents
• Job search/other search engines
• Image captions
• Product titles
• Need to wrangle text into matrix form in many
applications
• Embeddings
• Parts-of-speech counts
• Sentiment analysis results

3. Common Tools:
Sentiment Analysis
• Understand positive/negative/neutral tone of text
data
• Expansion to other emotions:
• Anger
• Sadness
• Surprise
• Some uses:
• Identifying customer churn
• Evaluating educational interventions
• Predicting clinical outcomes
• Some packages exist for some languages and
applications.
• Other languages or emotions require custom code
and dictionaries.

4. Common Tools:
Embeddings

5. Embeddings
Capture relative frequency of word use within a text
and across texts
• Can use down-weighting to ignore common words like “a” or
“the”
• Don’t capture context well in the simple versions
• She bolted the door shut.
• She bolted out the door.
Pretrained encoder/decoder neural networks that
can capture context
• BERT
• GPT-3
Most pretrained models only support a limited
number of languages (though have ways of training
a similar model on a new language corpus)…

6. Consider the
apps you use
every day…
Now imagine
they didn’t
exist in your
language…

7. NLP Needs in
Underserved
Areas
• Translation and speech-to-text for
unsupported languages (Hausa,
Lingala, Quechua…)
• Sentiment dictionaries for unsupported
languages/emotional nuances of the
language
• NLP-powered apps (search engines,
matching/recommenders, symptom
checkers, conversational agents…)
• Language preservation of endangered
languages
• 308 highly endangered ones just in
Africa

8. Market Size for NLP Applications
• Worldwide NLP market projected
to grow from $21B in 2021 to
$127B by 2028.
• South America and Africa are
mostly ignored markets for NLP-
backed technology in healthcare,
travel, retail, education, and other
markets.
• Local companies and universities
are currently trying to meet market
needs.

9. Caveats…
• Collecting the data
• Existing sources, creating written sources for non-written languages (3074 of 7139 languages that exist)
• Capturing speech tone variety, storing large audio files for non-written languages
• Getting large enough sample sizes from endangered languages (Domari in Northern Africa/Middle East)
• Ownership of data
• Foreign corporations? Governments? Universities? Local speakers?
• Biases and misuses
• Unintentional translation issues from non-native speakers reviewing technology (ex. diseases/symptoms)
• Lack of representation in languages targeted/training in NLP (wealthy world vs. developing world)
• Use of technologies to spread conflict (companies, world powers, neighboring countries… interfering)

10. Case Studies: Recent
Collaborations
Sub-Saharan Africa

11. Customized
Dictionaries and
Embeddings
• AfroLeadership
• Crowd-source local language
sentiment dictionaries, writing
samples for embeddings…
• Led by students and researchers
at local Cameroonian universities
• Hausa Hackathon
• Non-profit initiative to build
corpus/dictionaries and build
applications to support the Hausa
language
• Hackathons for Hausa speakers
and NLP professionals interested
in Hausa applications
• Masakhane
• Non-profit collaboration of NLP
researchers in Africa
• Broad set of target languages

12. Companies Powered by NLP
• Mpuza Inc
• Job matching app connecting companies and job
seekers
• Powered by NLP-based matching engine
• Caveat of needing filters for extremism recruiting:
• Rwanda history and neighboring DRC violence
• Need to identify extremist recruitment job posts
• Name changes of extremist groups
• Concealed recruitment/threats…
• False positives for human rights and security positions

13. Miami’s Unique Position

14. Questions…
• How many familiar with NLP?
• How many lived in another country
as a child?
• How many interested in making
money or making a social good
impact?

15. We’re positioned accelerate NLP
development for underserved populations.
Starting companies
Volunteering time
Creating NLP hackathons
All from where we are in Miami…

16. Contact Information
cfarrelly@med.miami.edu