You want to improve a chatbot's understanding by using embeddings. Which approach best captures semantic meaning for similar questions like "How are you?" and "How do you do?"?

hard📝 Application Q15 of 15

NLP - Word Embeddings

AUse only the first word's embedding as sentence meaning

BUse pretrained word embeddings and average their vectors for the whole sentence

CUse random vectors for each word without training

DUse one-hot encoding for each word and sum them

Step-by-Step Solution

Solution:

Step 1: Understand sentence embedding from word embeddings
Averaging pretrained word embeddings creates a vector representing the whole sentence's meaning.
Step 2: Compare other options
One-hot encoding loses semantic info, random vectors have no meaning, and using only first word misses context.
Final Answer:
Use pretrained word embeddings and average their vectors for the whole sentence -> Option B
Quick Check:
Average pretrained embeddings = better sentence meaning [OK]

Quick Trick: Average pretrained embeddings for sentence meaning [OK]

Common Mistakes:

MISTAKES

Using one-hot encoding which lacks meaning
Using random vectors without training
Ignoring all words except the first

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More NLP Quizzes

You want to improve a chatbot's understanding by using embeddings. Which approach best captures semantic meaning for similar questions like "How are you?" and "How do you do?"?

Step 1: Understand sentence embedding from word embeddings

Step 2: Compare other options

Final Answer:

Quick Check:

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