One of the most underrated NLP techniques, in my experience, is sentence embeddings and semantic similarity search. While large language models and flashy transformer architectures get most of the attention, embeddings are incredibly practical for real-world applications.

Why they’re underrated:

• Cross-document retrieval: You can find relevant information even when keywords don’t exactly match.
• Clustering and topic discovery: Embeddings make it easier to group similar sentences or documents and discover latent themes.
• Efficiency: Once computed, similarity searches are fast and computationally cheaper than running a large model each time.
• Explainability: You can inspect nearest neighbors to understand why a result was retrieved, which is helpful for analysis and trust.

They’re extremely versatile for building search engines, recommendation systems, summarization pipelines, or even improving chatbot responses without requiring heavy inference for every query.

Other underrated techniques I’ve seen are attention visualization for interpretability and clever tokenization strategies for domain-specific text, but embeddings really stand out for day-to-day NLP work.