Microsoft Azure AI Fundamentals (AI-900)Describe features of Natural Language Processing (NLP) workloads on AzureMedium
A researcher is developing a system to quickly find all mentions of specific drug names, disease names, and treatment procedures within a large corpus of medical journal articles. Which Azure NLP capability is most appropriate for identifying these predefined categories of terms?
- ALanguage Detection
- BText Summarization
- CSentiment Analysis
- DNamed Entity Recognition (NER)
Show answer & explanationAnswer & explanation
Correct answer: D. Named Entity Recognition (NER)
Named Entity Recognition (NER) is designed to identify and categorize specific entities in text, such as names of persons, organizations, locations, and in this case, medical concepts like drug names, diseases, and treatments.
Why the other options are wrong
- A. Language Detection identifies the language, not specific terms within it.
- B. Text Summarization condenses text, it doesn't identify specific entities.
- C. Sentiment Analysis determines emotional tone, not specific entities.
Named Entity Recognition (NER)
Named Entity Recognition (NER) is an NLP task that locates and classifies named entities in unstructured text into predefined categories such as person names, organizations, locations, medical codes, etc.
- Crucial for information extraction and structuring data.
- Can be pre-trained (general entities) or custom-trained (specific domains).
- Helps in indexing, search, and knowledge graph construction.
Memory trick: Named Entities Reveal Crucial Data.