Custom Embeddings Outperform Generic Ones

Word embeddings trained on the target corpus (using BlazingText) significantly outperformed generic pre-trained embeddings (GloVe) for highlight classification. Precision improved from 0.72 to 0.78; recall from 0.70 to 0.73.

The mechanism: every embedding vector encodes semantic relationships among words based on the context of how those words appear. Training on biological text might show “apple” and “pear” as similar. Training on technology text might not.

Market research transcripts have their own vocabulary, phrasing patterns, and contextual relationships. Generic embeddings trained on Wikipedia or news corpora don’t capture these domain-specific semantics.

The practical implication: when you have enough domain-specific data, training custom embeddings is often worth the effort. The “convenience” of pretrained embeddings comes with a domain mismatch cost.

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