Word Embeddings QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

What is "ELMo" in the context of word embeddings?

Sentence length measure

Deep contextualized embeddings

Punctuation mark type

Tokenization synonym

Question: 2

What is the "bag of words" model?

Sentence length measure

Unordered word collection model

Punctuation mark type

Tokenization synonym

Question: 3

What is "concreteness rating" in word embeddings?

Punctuation removal technique

Abstractness measure

Punctuation mark type

Tokenization synonym

Question: 4

What is "hyperparameter tuning" in word embeddings training?

Sentence length measure

Training parameter optimization

Punctuation mark type

Stopword removal technique

Question: 5

What is "conceptual analogy" in word embeddings evaluation?

A method for assessing the semantic similarity of words

A way to measure the length of word vectors

A type of punctuation mark

A synonym for tokenization

Question: 6

What is "word vector composition" in word embeddings?

A type of punctuation removal technique

The process of combining word vectors to represent phrases or sentences

A measure of sentiment polarity in text

A synonym for tokenization

Question: 7

What is "document embedding" in word representations?

Vector length measure

Document vector representation

Punctuation mark type

Stopword removal technique

Question: 8

What is the "context window" in Word2Vec?

Sentence length measure

Training context range

Punctuation mark type

Tokenization synonym

Question: 9

What is "polysemy" in word embeddings?

A type of punctuation mark

The property of words having multiple meanings

A measure of sentiment polarity in text

A technique for converting text to binary code

Question: 10

What is "word sense disambiguation" in the context of word embeddings?

Sentence length measure

Meaning determination in context

Punctuation mark type

Tokenization synonym

Question: 11

What is the primary limitation of word embeddings like Word2Vec and GloVe?

No OOV representation

Extensive pre-processing

Computational inefficiency

Memory-intensive

Question: 12

What is the "embedding dimension" in word embeddings?

Vector length measure

Punctuation mark type

Vector space dimensions

Tokenization synonym

Question: 13

What is the primary goal of "fine-tuning" in contextual word embeddings like BERT?

Remove stopwords

Improve vector accuracy

Adapt pre-trained models

Measure sentiment polarity

Question: 14

What is "word vector analogy" in word embeddings evaluation?

Vector length measurement

Analogical word puzzle assessment

Punctuation mark type

Tokenization synonym

Question: 15

What is "BERT" in word embeddings and language understanding?

Punctuation removal technique

Grammatical analysis model

Punctuation mark type

State-of-the-art pre-trained model

Question: 16

What is "contextual word embeddings" in word representations?

Context-capturing word embeddings

Contextless word vectors

Sentiment polarity measure

Punctuation removal technique

Question: 17

What is "t-SNE" in word embeddings visualization?

Sentence length measure

Dimensionality reduction for visualization

Punctuation mark type

Tokenization synonym

Question: 18

What is the "Word Mover's Distance" (WMD) in word embeddings?

Sentence length measure

Document dissimilarity metric

Punctuation mark type

Tokenization synonym

Question: 19

What is the primary advantage of subword embeddings in word representations?

Memory-efficient

Faster training

Represent OOV words

Simple implementation

Question: 20

In word embeddings, what is the "word similarity" task used for evaluation?

Vector length measurement

Cosine similarity evaluation

Word frequency determination

Sentiment polarity measurement

Question: 21

What is the primary goal of "fastText" in word embeddings?

Speed up Word2Vec

Consider subword info

Sentiment analysis

Remove stopwords

Question: 22

What does "GloVe" stand for in the context of word embeddings?

Overfitting Embeddings

Logarithmic Encoding

Global Word Vectors

Language Validation Estimation

Question: 23

Which of the following word embedding techniques is based on matrix factorization?

Word2Vec

GloVe

Skip-gram

Bag of Words (BoW)

Question: 24

What is "continuous bag of words" (CBOW) in Word2Vec?

A model for grammatical analysis of sentences

A model that predicts a target word given its context words

A type of punctuation mark

A synonym for tokenization

Question: 25

In Word2Vec, what is the "window size" parameter used for?

Dimensionality of vectors

Number of epochs

Context window size

Learning rate control

Question: 26

What is the "skip-gram" model in Word2Vec?

Grammatical analysis

Context prediction

Punctuation removal

Tokenization synonym

Question: 27

Which word embedding technique uses a neural network to learn word representations?

Latent Semantic Analysis

Word2Vec

Term Frequency-Inverse Document Frequency

Bag of Words

Question: 28

In word embeddings, what does it mean for words with similar meanings to have similar vectors?

No similarities

Orthogonal vectors

Similar vectors

Random vectors

Question: 29

What are word embeddings in natural language processing?

The process of converting text to binary code

Distributed representations of words as dense vectors in a continuous vector space

The removal of stopwords from text

A measure of sentiment polarity in text

Question: 30

What is the primary advantage of word embeddings compared to one-hot encoding?

Simpler implementation

Captures semantic relationships

Smaller vector sizes in one-hot encoding

Memory efficiency in one-hot encoding

Word Embeddings QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

What is "ELMo" in the context of word embeddings?

What is the "bag of words" model?

What is "concreteness rating" in word embeddings?

What is "hyperparameter tuning" in word embeddings training?

What is "conceptual analogy" in word embeddings evaluation?

What is "word vector composition" in word embeddings?

What is "document embedding" in word representations?

What is the "context window" in Word2Vec?

What is "polysemy" in word embeddings?

What is "word sense disambiguation" in the context of word embeddings?

What is the primary limitation of word embeddings like Word2Vec and GloVe?

What is the "embedding dimension" in word embeddings?

What is the primary goal of "fine-tuning" in contextual word embeddings like BERT?

What is "word vector analogy" in word embeddings evaluation?

What is "BERT" in word embeddings and language understanding?

What is "contextual word embeddings" in word representations?

What is "t-SNE" in word embeddings visualization?

What is the "Word Mover's Distance" (WMD) in word embeddings?

What is the primary advantage of subword embeddings in word representations?

In word embeddings, what is the "word similarity" task used for evaluation?

What is the primary goal of "fastText" in word embeddings?

What does "GloVe" stand for in the context of word embeddings?

Which of the following word embedding techniques is based on matrix factorization?

What is "continuous bag of words" (CBOW) in Word2Vec?

In Word2Vec, what is the "window size" parameter used for?

What is the "skip-gram" model in Word2Vec?

Which word embedding technique uses a neural network to learn word representations?

In word embeddings, what does it mean for words with similar meanings to have similar vectors?

What are word embeddings in natural language processing?

What is the primary advantage of word embeddings compared to one-hot encoding?