Microsoft Certified: Azure AI Engineer AssociateImplement natural language processing solutionsEasy
A financial institution wants to develop a system that can automatically identify and extract specific financial terms, such as 'interest rate,' 'loan agreement number,' and 'stock symbol,' from lengthy legal documents and customer correspondence. The institution needs to ensure high accuracy for these domain-specific terms. Which Azure AI Language feature should the institution use?
- AText Summarization
- BKey Phrase Extraction
- CCustom Named Entity Recognition (NER)
- DSentiment Analysis
Show answer & explanationAnswer & explanation
Correct answer: C. Custom Named Entity Recognition (NER)
Custom Named Entity Recognition (NER) is designed to identify and extract domain-specific entities that are not part of the pre-trained models. This allows for high accuracy in specialized fields like finance. Key Phrase Extraction identifies general important phrases, not specific entities.
Why the other options are wrong
- A. Text Summarization condenses text, it does not extract specific entities.
- B. Key Phrase Extraction identifies general important phrases, which may not be specific financial terms.
- D. Sentiment Analysis determines the emotional tone of text, not specific entities.
Custom Named Entity Recognition (NER)
A feature of Azure AI Language that allows users to train a model to identify and extract domain-specific entities from unstructured text.
- Identifies and extracts custom entity types.
- Requires labeled data for training.
- Useful for specialized domains like legal, medical, or finance.
Memory trick: Custom NER is like a bespoke treasure hunt for specific words in your text.