Unlock NER for Sensitive Data with NLOP - GDSC 2023

Today we’re going to learn about Natural Language Processing. NLP is a technology that teaches machines to understand human languages.
Specifically…we will learn how to use an NLP model to teach a machine to recognize when a human shares potentially sensitive information in a consumer or enterprise system.

Transcript

1. NLP for Sensitive Data NORP DATE ORG GPE MONEY GPE PERSON QUANTITY Noble Ackerson Applied AI Product Lead, Former Google Developers Expert for Product Strategy Sensitive Text Detection with Custom Natural Language Processing (NLP) Models
2. Agenda 1 Demo: Custom Entity Recogniser Process Real world use-cases 2 3
3. Demo Custom Named Entity Recogniser
4. What is Natural Language Processing (NLP)? Input ❏ Natural Language ❏ Text ❏ Speech Output ❏ General text classification ❏ Entity extraction ❏ Machine translation ❏ Question and Answering ❏ Embeddings & Semantic Search ❏ Conversational machine interaction ❏ … Process ❏ Text representation ❏ Machine Learning Models ❏ Generative ❏ Semantic meaning ❏ Contextual understanding Generative AI (NLU with LLMs)
5. Process 1 Identifying the right NLP Use Cases Annotate data with the help of Generative AI Train a custom Named Entity Recognition (NER) model & refine Evaluate, test, custom NER model Deploy and integrate 2 3 4 5 Today’s Tools • Google Colab (shared) • spaCy NLP library • Google Cloud Platform
6. Identifying the right NLP Use Cases Human-centered needs analysis for A.I. Don’t “A.I.” all the things.
7. Identifying the right NLP Use Cases User patterns Scope
8. Production grade ML/NLP DATA MANAGEMENT DATA COLLECTION EXPLORATION & ANALYSIS TOOLS NLP CODE FEATURE ENGINEERING (Labeling, Annotations) MODEL TRAINING AT SCALE AUTOMATION LOGGING & MANAGEMENT MONITORING SERVING INFRASTRUCTURE NEEDS ANALYSIS Adapted from: “Hidden Technical Debt in Machine Learning Systems”, D. Sculley et. al, Google
9. Identifying the right NLP Use Cases Automation Augmentation User doesn’t know how to do something User feels responsible for task User can’t do something High stakes situation Task is boring, repetitive, or dangerous Complicated personal preferences If Machine Learning and NLP is needed, which type is best?
10. Setting expectations NLP CODE DATA MANAGEMENT DATA COLLECTION EXPLORATION & ANALYSIS TOOLS
11. Process 1 Identifying the right NLP Use Cases Annotate data with the help of Generative AI Train a custom NER model & refine Evaluate, test, custom NER model Deploy and integrate 2 3 4 5
12. Annotating your text for Entity Recognition Labeling
13. Teacher: Class, pay attention Transformer Models: A brief bit about how NLU, LLM, GenAI play into NER workflow Deep Learning Machine Learning Natural Language Processing (NLP) Large Language Models (LLM) Artificial Intelligence
14. Process 1 Identifying the right NLP Use Cases Annotate data with the help of Generative AI Train a custom NER model & refine Evaluate, test, custom NER model Deploy and integrate 2 3 4 5
15. The NLP workflow for training custom Entity Recognition …with Large Language Models
16. Starting with Pre-trained NER Models
17. Process 1 Identifying the right NLP Use Cases Annotate data with the help of Generative AI Train a custom NER model & refine Evaluate, test, custom NER model Deploy and integrate 2 3 4 5
20. Wrap up 1 Demo: Custom Entity Recogniser Process Real world use-cases 2 3
21. Healthcare Clinical Documentation Custom Entities for Clinical Documentation NORP DISEASE CONDITION GPE DOSAGES PERSON MEDICATION EHR
22. Legal Contract Analysis Custom Entities for Legal Documents RIGHTS CLAUSE CONDITION GPE JURISDICTION PARTIES CONTRACT DATE
23. Sentiment Analysis Use Case cloud.google.com/natural-language
24. Translation Use Case https://cloud.google.com/translate
25. All-industries Try Natural Language AI on Google Cloud cloud.google.com/natural-language
26. Good luck and Thank you! Noble Ackerson AI Product Lead, GDE Alumni medium.com/@nobleackerson youtube.com/c/nobleackerson Resources What is Natural Language Processing? [Google Cloud] Natural Language Processing on Google Cloud [Cloud Skills Boost] TensorFlow Models NLP Library [tensorflow.org]
27. Identifying Use Cases for LLMs Risk Tolerance Human Review Text (or Code) Intensive Business Value
28. Token Classification with Custom NER models Inputs Training & Validation Datasets Deploy API and Versioning Prediction Clients (Online Systems) REST API call with input variables Trained Models Training Serving Preprocess & Feature Creation Preprocess Labeling & Annotation Train/Tune Model
29. In case of demo fail