The document introduces natural language processing and generative AI, highlighting key concepts, applications, and technologies like GPT-4 and the significance of sentiment analysis. It discusses the importance of data handling, classification tasks, and the ethical considerations involving biases and misuse of AI. Furthermore, it emphasizes the utility of pre-trained models and the need for contextual understanding in text processing.

1. Natural Language Processing for
Beginners
Colleen M. Farrelly

2. About Me
• Data science
lead/entrepreneur
• Geometry and NLP
researcher
• Author
• The Shape of Data (NSP,
2023)
• Network science book
(Packt, 2024)
• Artist and Calligrapher

3. Generative
AI
A gentle introduction

4. What is
generative AI?
• Set of algorithms that generate:
• Images
• Text samples
• Videos
• Audio content
• Guided by:
• Training sample
• User specifications
• Deep learning architectures

5. GPT
• Generative Pre-trained
Transformer 4
• Decoder-only transformer
network
• Gives sequence-to-
sequence decoder with
long-range memory
• Already blurring lines
between human
composition and AI

6. Tools that should work in Anywhere
Speech generation:
• https://play.ht/text-to-speech-voices
Text generation (OpenAI alternative, GPT-2):
• https://huggingface.co/tasks/text-generation
Image generation:
• https://creator.nightcafe.studio/create

7. Sentiment
Analysis and
Text Classifiers
Theory and Practice

8. Sentiment Analysis
• Understand positive/negative/neutral
tone of text data
• Customer feedback
• Chatbot emotion regulation
• Predicting patient outcomes or
physician bias
• Expansion to other emotions:
• Anger
• Sadness
• Surprise
• Some packages exist for some languages
and applications.
• Other languages or emotions require
custom code and dictionaries.

9. Classification
• Surgical outcomes based on
physician notes
• Types of customer complaints
from automated feedback form
• Tasks for chatbot to route from
sales chatbot conversations

10. Wrangling Text to Numeric Matrix
• Document word
counts/frequencies
• Binary, count, or
weighted
frequency/
inverse
frequency
• Sparse numeric
matrix

11. Embeddings:
High
dimension to
low
dimension

12. Context Matters: Pretrained encoder/decoder neural networks

13. BERT Models
• Many BERT and RoBERTa models
on HuggingFace
• Pre-trained neural networks
• Good context for English
• Fairly complex sentences
• Fantasy or trademark words
• Some domain-specific versions
• Easy to use in Python
• Requires computer storage
space

14. Fun with Python

15. Ethical Considerations

16. Some Ethical
Considerations
• Training data bias (pretrained
embeddings or own data)
• Misuse of generative AI (fake
news)
• Representation in models
• Language accuracy biases in
multilingual models
• Misclassification biases
• Plagiarism bias against non-
native English speakers