[Solved] Why might topic coherence scores sometimes be misleading when comparing models? — Ans: Because coherence depends on the reference corpus and preprocessing | NLP

NLP - Topic Modeling

Why might topic coherence scores sometimes be misleading when comparing models?

ABecause coherence measures training time, not quality

BBecause coherence always increases with more topics

CBecause coherence ignores word meanings

DBecause coherence depends on the reference corpus and preprocessing

Step-by-Step Solution

Solution:

Step 1: Understand factors affecting coherence
Coherence depends on the corpus used and how text is preprocessed, affecting scores.
Step 2: Eliminate incorrect statements
Coherence does not always increase with topics, does not measure training time, and does consider word meanings.
Final Answer:
Because coherence depends on the reference corpus and preprocessing -> Option D
Quick Check:
Coherence influenced by corpus and preprocessing [OK]

Quick Trick: Coherence varies with corpus and preprocessing [OK]

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