[Solved] Consider this Python code snippet:king = np.array([0.5, 0.8]) man = np.array([0.6, 0.9]) woman = np.array([0.45, 0.... | NLP

NLP - Word Embeddings

Consider this Python code snippet:

king = np.array([0.5, 0.8])
man = np.array([0.6, 0.9])
woman = np.array([0.45, 0.75])
queen = king - man + woman
print(queen)

Why might this code not produce the expected analogy result?

ABecause the vectors have different dimensions

BBecause numpy arrays cannot be subtracted or added directly

CBecause the code does not find the closest word vector to the resulting vector

DBecause the print statement is missing parentheses

Step-by-Step Solution

Solution:

Step 1: Analyze the operation
The code performs vector arithmetic but does not search for the closest matching word vector.
Step 2: Check other options
NumPy supports vector addition/subtraction; vectors have same dimensions; print syntax is correct.
Final Answer:
Because the code does not find the closest word vector to the resulting vector -> Option C
Quick Check:
Vector math alone doesn't find the best matching word [OK]

Quick Trick: Vector math needs nearest neighbor search [OK]

Common Mistakes:

MISTAKES

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