
NLP Concepts Quiz
Authored by Thiện Trần Khải
Computers
University
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20 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main limitation of one-hot encoding in NLP?
It is too short
It captures semantics well
It cannot represent numerical data
It does not capture semantic relationships
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a frequency-based method for word representation?
Word2Vec
BERT
Count Vector (Bag of Words)
GPT
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Word embeddings map words to:
Integers
Binary values
High-dimensional real-valued vectors
One-hot positions
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which technique improves semantic similarity representation?
One-hot
Hashing
Word Embedding
Symbolic logic
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which is NOT a word embedding model?
Word2Vec
GloVe
TF-IDF
BERT
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why are word embeddings important in NLP?
They are easy to visualize
They reduce memory
They help models capture context and meaning
They simplify algorithms
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A neural network is inspired by:
Animal instincts
Artificial neurons
Human reflex
Brain's structure and functioning
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