Advances in Azure NLP Services
New capabilities in conversational AI and text analytics are transforming how businesses interact with data.
Natural Language Processing (NLP) on Azure has evolved significantly. From Conversational Language Understanding (CLU) to powerful specific tasks like Named Entity Recognition (NER) and Sentiment Analysis, the toolkit for developers is growing rapidly.
Key Takeaways
- › CLU maps utterances to intents for dialog apps.
- › NER extracts entities/slots, not just keywords.
- › Sentiment Analysis handles polarity confidence.
Conversational Language Understanding
CLU is the backbone of modern chatbots on Azure. It interprets the "intent" of a user's message—what they want to do—and extracts "entities"—the specific data needed to fulfill that request. Remember, CLU processes text; for voice interactions, it must be paired with Speech-to-Text services.
Sentiment Analysis & Decision Making
Understanding customer sentiment is no longer just about "positive" or "negative" labels. Azure's Sentiment Analysis provides confidence scores at the sentence level, allowing for nuanced understanding of mixed feedback in documents.
Custom vs. Prebuilt
While prebuilt models handle general tasks excellently, Azure encourages custom training for specific domains. Whether it's Custom NER for industry-specific jargon or Custom Text Classification, the platform offers flexibility where accuracy is paramount.