NLP basics: teaching computers language quiz

Question 1

What is Natural Language Processing (NLP)?

Accepted Answer

Helping computers understand and generate human language

Answer

Teaching robots to walk and move

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Writing computer code automatically without a programmer

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Building faster computer chips and processors

Question 2

What is tokenization in NLP?

Accepted Answer

Splitting text into smaller units like words or punctuation

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Encrypting text with a secret password

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Translating text from one language to another

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Counting the total number of letters in a sentence

Question 3

What are 'stop words' in NLP?

Accepted Answer

Common words removed before text analysis because they carry little meaning

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Words that cause the computer program to crash

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Very rare and unusual technical words

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Words written in a foreign language inside a document

Question 4

What is sentiment analysis?

Accepted Answer

Automatically identifying the feelings or opinions expressed in text

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Detecting which language a piece of text is written in

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Counting how many sentences exist in a document

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Creating a short summary of a long article automatically

Question 5

What is a 'corpus' in NLP?

Accepted Answer

A large collection of text used to train or evaluate NLP models

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A single sentence fed into an NLP model for testing

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The final output produced by a machine translation system

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A special type of neural network layer used in transformers

Question 6

How does an email spam filter use NLP?

Accepted Answer

It learns word and phrase patterns to automatically detect unwanted emails

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It blocks emails based on the sender's country of origin

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It checks the size and type of email attachments

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It only allows emails from contacts already in your address book

Question 7

What does autocorrect do in NLP terms?

Accepted Answer

It detects and fixes spelling mistakes using language pattern matching

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It automatically sends your messages on your behalf

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It translates your text into another language automatically

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It enlarges the font size whenever the user makes a typing error

Question 8

What is machine translation?

Accepted Answer

Automatically converting text from one human language to another using AI

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A physical machine that prints books translated into different languages

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Converting spoken words into written text on a screen

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A program that teaches human users to speak multiple languages

Question 9

What is a chatbot?

Accepted Answer

A computer program that converses with humans using natural language

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A physical robot designed to have face-to-face conversations with people

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A music streaming app that responds to voice commands only

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A system that converts sign language into spoken speech

Question 10

Why is text 'cleaning' an important first step before training an NLP model?

Accepted Answer

To remove noise and inconsistencies so the model learns meaningful patterns

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To make the text look prettier and more colorful on screen

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To reduce the file size of text documents before storage

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To add emojis to text so the model better understands emotions

Question 11

What is stemming in NLP?

Accepted Answer

Cutting words down to their root form by removing endings

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Translating words into numerical vectors for a model

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Removing all punctuation marks from sentences

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Finding synonyms and replacing them in the original text

Question 12

What is the key difference between stemming and lemmatization?

Accepted Answer

Lemmatization always returns a real dictionary word; stemming may not

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Stemming works only on nouns; lemmatization works only on verbs

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They are completely identical techniques with different names

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Stemming requires AI; lemmatization uses only a printed dictionary

Question 13

What is the Bag of Words (BoW) model?

Accepted Answer

A representation that counts word frequencies but ignores the order they appear in

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A model that stores words sorted alphabetically into labeled bags

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A model that carefully tracks the exact order of every word in a sentence

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A tool for automatically finding words that rhyme in poetry

Question 14

What does TF-IDF stand for and why is it useful in NLP?

Accepted Answer

Term Frequency – Inverse Document Frequency

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Text Feature – Index Document Frequency

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Total File – Internal Data Format

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Token Frequency – Intelligent Data Filter

Question 15

What is Named Entity Recognition (NER)?

Accepted Answer

Identifying and labeling real-world entities like names and places in text

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Giving new names to computer files based on their content

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Recognizing human faces in photographs using NLP techniques

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Counting how many proper nouns appear in a given sentence

Question 16

What are N-grams and why are they useful in NLP?

Accepted Answer

Sequences of N consecutive words used to capture local context in text

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The total number of times a single word is repeated in a sentence

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A method for translating between N different languages all at once

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A technique that splits text into its individual letters one by one

Question 17

What is text classification in machine learning?

Accepted Answer

Automatically assigning predefined categories or labels to pieces of text

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Organizing physical books on a library shelf by their size and color

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Checking whether two texts were written by the same author

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Converting text documents into image files for digital display

Question 18

What is one-hot encoding for words in NLP?

Accepted Answer

Representing each word as a list with a single 1 and all other values as 0

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Encoding the processor temperature during model training

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Assigning one unique integer number to each word in the vocabulary

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Coloring different words with different colors based on their meaning

Question 19

What is a word embedding and how does it improve on one-hot encoding?

Accepted Answer

A dense vector of decimal numbers that captures word meaning and similarity

Answer

Hiding secret messages inside ordinary words in a document

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Embedding HTML formatting tags directly into plain text documents

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A compression method for making large text files smaller

Question 20

What is a language model and what does it actually predict?

Accepted Answer

An AI trained to predict the next word in a sequence of text

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Software that teaches human users to learn new foreign languages

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A model that understands only one specific programming language

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A giant database containing every word in a language

Question 21

What is Part-of-Speech (POS) tagging?

Accepted Answer

Labeling each word in a sentence with its grammatical role like noun or verb

Answer

Adding colorful highlighting to all nouns found in a text editor

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Automatically detecting typos by checking part-of-speech grammar rules

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Splitting a document into separate paragraphs for easier reading

Question 22

Why do NLP models need very large amounts of training data?

Accepted Answer

Language has so many variations that more examples help the model generalize

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More data makes the computer hardware run physically faster

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Small datasets would cause the model to become far too accurate

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Training data is only needed for image models, not language models

Question 23

Why do NLP engineers convert all text to lowercase during preprocessing?

Accepted Answer

To ensure the same word in different capitalizations is treated identically

Answer

To make the text easier for human readers to read on screen

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To significantly reduce the storage size of large text files

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To remove all numbers and digits from the text automatically

Question 24

What is automatic text summarization and what are its two main types?

Accepted Answer

Automatically creating a shorter version of a long text while preserving key points

Answer

Counting the total number of words and sentences in a document

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Automatically rewriting a document in a simpler vocabulary

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Highlighting all the adjectives in a document for the reader

Question 25

What is Word2Vec and what surprising mathematical trick can it perform?

Accepted Answer

A neural method producing word vectors where arithmetic captures meaning relationships

Answer

A tool that counts how often any two words appear in the same sentence together

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A method for converting entire sentences directly into images for display

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A dictionary that assigns a random number to each word in the vocabulary

Question 26

What does the attention mechanism do in modern NLP models?

Accepted Answer

It selectively focuses on the most relevant words when processing each part of a sentence

Answer

It speeds up training by ignoring less frequently occurring words

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It removes punctuation to shorten the sequence length during training

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It monitors the user's typing speed to improve real-time autocorrect

Question 27

What is the Transformer architecture and why was it a breakthrough in NLP?

Accepted Answer

An NLP architecture that uses attention to process all words in a sentence at once

Answer

A device that converts electrical signals into readable text output

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A model that converts image captions into text descriptions

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A type of recurrent neural network specifically designed for very long sentences

Question 28

What is transfer learning in NLP and why is it so valuable?

Accepted Answer

Reusing a pre-trained model and adapting it to a new specific task with less data

Answer

Physically moving a trained model from one computer server to another

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Sharing training datasets between two separate AI research organizations

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Converting a model trained on English text to work directly on images

Question 29

What is BERT and how does it differ from GPT in its approach to language?

Accepted Answer

BERT reads bidirectionally for understanding; GPT reads left-to-right for generation

Answer

BERT generates text; GPT classifies and understands text

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BERT and GPT are identical models that simply have different names

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BERT is designed for images; GPT is designed for text only

Question 30

What does it mean for an NLP model to be 'biased' and why is it an ethical concern?

Accepted Answer

The model makes unfair predictions because training data reflected social stereotypes

Answer

The model was intentionally programmed by engineers to favor certain users

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The model runs more slowly for certain languages due to a technical programming bug

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The model only works correctly when given perfectly formatted and clean input text

Test yourself