You need to identify industry-specific technical terms in documents. That is a classic use case for Custom Named Entity Recognition (custom NER) , which lets you build a model to extract domain-specific entities (for example, chemical names, part numbers, contract IDs) from unstructured text. Among the options, only custom NER is designed to recognize entities unique to your domain.
Why not Key Phrase Extraction? It returns the main concepts/talking points of text, not typed, domain-specific entities.
Why not CLU? Conversational Language Understanding is for intent and entity extraction from user utterances in conversations , not for mining technical terms from documents. Microsoft Learn
Why not Language Detection? It detects the language of the text only.
Custom NER fits: Purpose-built to extract domain-specific entities with minimal code; you label a small dataset and train in Language Studio.
Microsoft References:
Custom Named Entity Recognition overview – purpose is extracting domain-specific entities . Microsoft Learn
Transparency note for custom NER – emphasizes domain-specific extraction. Microsoft Learn
Key Phrase Extraction overview – identifies main concepts , not domain entities. Microsoft Learn
CLU overview – intent/entity for conversational scenarios. Microsoft Learn
