[Solved] What is the main purpose of an Embedding layer in NLP models? — Ans: To convert words into dense vectors that capture meaning | NLP

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What is the main purpose of an Embedding layer in NLP models?

ATo split sentences into individual characters

BTo count the number of words in a sentence

CTo convert words into dense vectors that capture meaning

DTo remove stop words from text

Step-by-Step Solution

Solution:

Step 1: Understand what embedding layers do
Embedding layers transform words or tokens into dense numeric vectors that represent semantic meaning.
Step 2: Compare options with embedding purpose
Counting words, removing stop words, or splitting characters are preprocessing steps, not embedding functions.
Final Answer:
To convert words into dense vectors that capture meaning -> Option C
Quick Check:
Embedding = word vectors [OK]

Quick Trick: Embedding layers create numeric word meanings [OK]

Common Mistakes:

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What is the main purpose of an Embedding layer in NLP models?