When people discuss Natural Language Processing (NLP), the conversation often centers around Large Language Models (LLMs), transformers, chatbots, embeddings, and retrieval-augmented generation (RAG). While these advancements have transformed the field, many powerful NLP techniques don’t seem to get the attention they deserve.

For example:

• Topic modeling can uncover hidden themes in large text corpora.
• Named Entity Recognition (NER) can extract valuable structured information from unstructured text.
• Dependency parsing helps reveal grammatical relationships between words.
• Semantic similarity techniques can improve search and recommendation systems.
• Text summarization can significantly reduce information overload.

In your experience:

🔹 Which NLP technique do you find most underrated?

🔹 What problems does it solve better than more popular approaches?

🔹 Can you share a real-world use case where it delivered valuable insights or business impact?

🔹 Which tools, libraries, or frameworks do you use to implement it?

I’m interested in hearing about techniques that deserve more attention and learning how others are applying them in production environments. Looking forward to the discussion!