You are building an app that will analyze documents by using the Azure Al Language service.
You need to identify industry-specific technical terms in the documents. The solution must minimize development effort.
What should you use?
Correct Answer: B
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