Topic 1 - Introduction to Natural Language Processing

Question 1

Natural Language Processing (NLP)

Answer 1

Process of using computers to extract meaning from text

Question 2

NLP History: 1950s-1980s

Answer 2

Focused on linguistics, grammar rules, and sentence structure parsing

Question 3

NLP History: 1980s-Now

Answer 3

Shifted to data-driven approaches using statistical and machine learning methods

Question 4

NLP History: Now-Future

Answer 4

Utilizes neural networks and deep learning techniques

Question 5

Natural Language Understanding (NLU)

Answer 5

Converting raw text or speech into a conceptual representation for computers

Question 6

Natural Language Generation (NLG)

Answer 6

Converting conceptual representation back into text or speech

Question 7

Document Classification

Answer 7

NLU application for categorizing documents (e.g., spam vs. not spam)

Question 8

Topic Modeling

Answer 8

NLU application for breaking documents into topics at the word level

Question 9

Intent Matching

Answer 9

NLU application for understanding various expressions of the same intent

Question 10

Natural Language Search

Answer 10

NLU application allowing users to search using natural language instead of keywords

Question 11

Machine Translation

Answer 11

NLG application for automatically translating text between languages

Question 12

Document Summarization

Answer 12

NLG application for generating text summaries of documents

Question 13

Text Generation

Answer 13

NLG application for producing coherent and contextual text

Question 14

Question Answering

Answer 14

NLG application for providing answers based on large text sources

Question 15

Image Captioning

Answer 15

NLG application for generating textual descriptions of images

Question 16

Large Language Models (LLMs)

Answer 16

Deep learning models trained to produce text, backbone of modern NLP

Question 17

LLM Training: Data Collection

Answer 17

Gathering vast and diverse text corpus from various sources

Question 18

LLM Training: Data Pre-processing

Answer 18

Cleaning and formatting data, including tokenization

Question 19

LLM Training: Model Architecture

Answer 19

Choosing and designing a Transformer-based model structure

Question 20

LLM Training: Regularization

Answer 20

Applying techniques to prevent overfitting (e.g., dropout, layer normalization)

Question 21

Fine-tuning

Answer 21

Process of further training a portion of the model with domain- or task-specific data

Question 22

GPT-3 Approach

Answer 22

Prompt-based learning that occurs dynamically during prediction phase

Question 23

Key Difference: NLU vs NLG

Answer 23

NLU converts text to conceptual representation, NLG does the reverse