Describe Features of Natural Language Processing (NLP) Workloads on Azure
Core Azure AI Language capabilities
- Language detection - which language is this text?
- Sentiment analysis and opinion mining - positive/neutral/negative, and at aspect level ("the food was great but the service was slow").
- Key phrase extraction - the main concepts in a document.
- Named entity recognition (NER) - people, places, organisations, dates, quantities.
- Entity linking - resolving mentions to a known knowledge-base entry.
- PII detection - finding and redacting personal data.
- Summarisation, question answering, and conversational language understanding (CLU) - CLU maps an utterance to an intent and extracts entities.
Speech and translation
Azure AI Speech covers speech-to-text, text-to-speech and speech translation; custom neural voice is limited access. Azure AI Translator handles text translation and can be combined with speech for real-time interpretation.
Traps that appear in questions
"What is this document about" is key phrase extraction, not entity recognition. Extracting a person's name or a date is NER. Routing a chatbot request such as "book a flight" is intent classification in CLU, and "to Paris" is an entity. Matching a user's free-text question to an existing FAQ is question answering. Detecting customer frustration in a support ticket is sentiment analysis. Also, sentiment applies to text, not to images, and PII detection is about redaction, not sentiment.
Worked example
A hotel wants to know whether guests praise the room but complain about breakfast. Whole-document sentiment would average this away, so the correct feature is opinion mining within sentiment analysis, which reports sentiment per aspect. If the goal were simply "list the topics mentioned", the answer would be key phrase extraction instead.