[Solved] In NLP, what is the primary function of an embedding layer within a neural network? — Ans: To convert discrete tokens into dense continuous vector representations | NLP

NLP - Sequence Models for NLP

In NLP, what is the primary function of an embedding layer within a neural network?

ATo perform tokenization of raw text data

BTo convert discrete tokens into dense continuous vector representations

CTo normalize input text by removing stopwords

DTo generate one-hot encoded vectors for each word

Step-by-Step Solution

Solution:

Step 1: Understand embedding layer role
Embedding layers map discrete tokens (like words) to dense vectors capturing semantic meaning.
Step 2: Eliminate incorrect options
Tokenization and stopword removal are preprocessing steps, not embedding functions. One-hot encoding is sparse, not dense.
Final Answer:
To convert discrete tokens into dense continuous vector representations -> Option B
Quick Check:
Embedding layers produce dense vectors [OK]

Quick Trick: Embeddings map words to dense vectors [OK]

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