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Why does cosine similarity work well for word vectors instead of Euclidean distance?

hard📝 Conceptual Q10 of 15
NLP - Word Embeddings
Why does cosine similarity work well for word vectors instead of Euclidean distance?
ACosine similarity measures angle, ignoring vector length differences
BEuclidean distance is faster but less accurate
CCosine similarity requires vectors to be normalized first
DEuclidean distance cannot be computed for word vectors
Step-by-Step Solution
Solution:

  1. Step 1: Understand cosine similarity property

    It measures the angle between vectors, focusing on direction, not magnitude.

  2. Step 2: Compare with Euclidean distance

    Euclidean distance is affected by vector length differences, which may not reflect semantic similarity.

  3. Final Answer:

    Cosine similarity measures angle, ignoring vector length differences -> Option A

  4. Quick Check:

    Cosine similarity focuses on angle, not length [OK]
Quick Trick: Cosine similarity ignores length, focuses on direction [OK]
Common Mistakes:
MISTAKES
  • Thinking Euclidean is always better
  • Believing cosine needs normalization
  • Assuming Euclidean can't be computed

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