Which technique is used to reduce the dimensionality of word vectors in NLP?
a) Clustering
b) Principal Component Analysis (PCA)
c) Tokenization
d) Word embeddings
Answer:
b) Principal Component Analysis (PCA)
Explanation:
PCA is used to reduce the dimensionality of word embeddings in NLP, making it easier to process and analyze large datasets.
Reference:
Natural Language Processing (NLP) Quiz – MCQ Questions and Answers