Quiz title: C4 Introduction to Natural Language Processing

Title: Q1 Multiple Choice
Points: 1
1. What is Natural Language Processing (NLP)?
a) A programming language for computers
*b) A field of AI that enables computers to understand and process human language
c) A method for translating between programming languages
d) A database management system
 
Title: Q2 Multiple Choice
Points: 1
2. Which of the following is NOT a typical challenge in NLP?
a) Ambiguity in language
b) Context dependency
c) Cultural variations in language use
*d) Mathematical calculations
 
Title: Q3 Multiple Choice
Points: 1
3. What is tokenization in NLP?
a) Converting text to numbers
*b) Breaking text into individual words or meaningful units
c) Removing punctuation from text
d) Translating text to another language
 
Title: Q4 Multiple Choice
Points: 1
4. What are stop words in NLP?
*a) Common words like "the," "and," "is" that are often removed during preprocessing
b) Words that cause the program to stop running
c) Words that are misspelled
d) Words in foreign languages
 
Title: Q5 Multiple Choice
Points: 1
5. What is stemming in text preprocessing?
a) Adding prefixes to words
*b) Reducing words to their root or base form
c) Capitalizing the first letter of words
d) Counting word frequencies
 
Title: Q6 Multiple Choice
Points: 1
6. What is lemmatization?
a) Removing all vowels from words
*b) Reducing words to their dictionary base form considering context
c) Converting text to uppercase
d) Translating words to another language
 
Title: Q7 Multiple Choice
Points: 1
7. What does TF-IDF stand for?
a) Text Frequency - Inverse Document Frequency
*b) Term Frequency - Inverse Document Frequency
c) Total Frequency - Individual Document Frequency
d) Token Frequency - Inverse Data Frequency
 
Title: Q8 Multiple Choice
Points: 1
8. What is the purpose of TF-IDF?
a) To count the total number of words in a document
*b) To measure the importance of words in documents relative to a collection of documents
c) To translate documents between languages
d) To check spelling and grammar
 
Title: Q9 Multiple Choice
Points: 1
9. What are n-grams in NLP?
a) A type of neural network
*b) Sequences of n consecutive words or characters
c) A method for spell checking
d) A way to measure document similarity
 
Title: Q10 Multiple Choice
Points: 1
10. What is a bigram?
a) A word that appears twice in a document
*b) A sequence of two consecutive words
c) A word with two syllables
d) A document with two paragraphs
 
Title: Q11 Multiple Choice
Points: 1
11. What is Part-of-Speech (POS) tagging?
a) Counting the number of words in each sentence
*b) Identifying the grammatical role of each word (noun, verb, adjective, etc.)
c) Translating words to different languages
d) Removing punctuation from text
 
Title: Q12 Multiple Choice
Points: 1
12. What is word embedding?
a) Inserting words into documents
*b) Representing words as numerical vectors that capture semantic meaning
c) Hiding words within other words
d) Counting word frequencies
 
Title: Q13 Multiple Choice
Points: 1
13. Which of the following is a popular word embedding technique?
a) TF-IDF
*b) Word2Vec
c) Tokenization
d) Stemming
 
Title: Q14 Multiple Choice
Points: 1
14. What is the main advantage of word embeddings over simple word counts?
a) They are faster to compute
*b) They capture semantic relationships between words
c) They require less memory
d) They work only with English text
 
Title: Q15 Multiple Choice
Points: 1
15. What is a corpus in NLP?
a) A single document
*b) A large collection of texts used for analysis or training
c) A type of algorithm
d) A programming language
 
Title: Q16 Multiple Choice
Points: 1
16. What is text normalization?
a) Making all text the same length
*b) Converting text to a standard format (lowercase, removing punctuation, etc.)
c) Translating text to English
d) Adding spaces between words
 
Title: Q17 Multiple Choice
Points: 1
17. What is the bag-of-words model?
a) A method for storing documents in bags
*b) A text representation that ignores word order and focuses on word frequency
c) A way to translate between languages
d) A technique for spell checking
 
Title: Q18 Multiple Choice
Points: 1
18. What is a major limitation of the bag-of-words model?
a) It's too slow to compute
*b) It ignores word order and context
c) It only works with short documents
d) It requires too much memory
 
Title: Q19 Multiple Choice
Points: 1
19. What is language modeling in NLP?
a) Creating physical models of languages
*b) Predicting the probability of word sequences in a language
c) Translating between different languages
d) Counting words in documents
 
Title: Q20 Multiple Choice
Points: 1
20. What is the difference between syntax and semantics in language?
a) Syntax is about meaning, semantics is about structure
*b) Syntax is about grammatical structure, semantics is about meaning
c) They are the same thing
d) Syntax is for computers, semantics is for humans
 
Title: Q21 Multiple Choice
Points: 1
21. What is text preprocessing?
a) Writing text before processing
*b) Cleaning and preparing text data for analysis
c) Processing text after analysis
d) Translating text to binary code
 
Title: Q22 Multiple Choice
Points: 1
22. Which of the following is typically done during text preprocessing?
a) Adding more words to the text
*b) Removing special characters and converting to lowercase
c) Translating to multiple languages
d) Increasing the font size
 
Title: Q23 Multiple Choice
Points: 1
23. What is the purpose of removing stop words?
a) To make text shorter
*b) To focus on more meaningful words by removing common, less informative words
c) To improve spelling
d) To translate text faster
 
Title: Q24 Multiple Choice
Points: 1
24. What is text segmentation?
a) Dividing text into equal-length pieces
*b) Dividing text into meaningful units like sentences or paragraphs
c) Removing segments of text
d) Combining multiple texts into one
 
Title: Q25 Multiple Choice
Points: 1
25. Why is NLP considered challenging compared to other AI tasks?
a) It requires more computational power
*b) Human language is ambiguous, context-dependent, and constantly evolving
c) It only works with English
d) It requires special hardware