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Why does the Jaccard similarity sometimes fail to capture semantic similarity between two text documents?

hard📝 Conceptual Q10 of 15
NLP - Text Similarity and Search
Why does the Jaccard similarity sometimes fail to capture semantic similarity between two text documents?
ABecause it only considers exact word overlap, ignoring word meaning
BBecause it uses cosine similarity instead of set operations
CBecause it requires numerical vectors, not sets
DBecause it always returns zero for large documents
Step-by-Step Solution
Solution:

  1. Step 1: Understand Jaccard similarity limitations

    It measures overlap of exact words, so it misses synonyms or related meanings.

  2. Step 2: Explain semantic similarity challenge

    Semantic similarity needs understanding of word meanings, which Jaccard does not capture.

  3. Final Answer:

    Because it only considers exact word overlap, ignoring word meaning -> Option A

  4. Quick Check:

    Jaccard ignores meaning, only counts exact matches [OK]
Quick Trick: Jaccard counts words, not meanings [OK]
Common Mistakes:
MISTAKES
  • Confusing with cosine similarity
  • Thinking it uses vectors
  • Assuming it returns zero for large docs
  • Ignoring semantic meaning

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