Natural language processing
Key points to understand about natural language processing (NLP) include:
- NLP capabilities are based on models that are trained to do particular types of text analysis.
- While many natural language processing scenarios are handled by generative AI models today, there are many common text analytics use cases where simpler NLP language models can be more cost-effective.
- Common NLP tasks include:
- Entity extraction - identifying mentions of entities like people, places, organizations in a document
- Text classification - assigning document to a specific category.
- Sentiment analysis - determining whether a body of text is positive, negative, or neutral and inferring opinions.
- Language detection - identifying the language in which text is written.
Note
In this module, we've used the term natural language processing (NLP) to describe AI capabilities derive meaning from "ordinary" human language. You might also see this area of AI referred to as natural language understanding (NLU).
Natural language processing scenarios
Common uses of NLP technologies include:
- Analyzing document or transcripts of calls and meetings to determine key subjects and identify specific mentions of people, places, organizations, products, or other entities.
- Analyzing social media posts, product reviews, or articles to evaluate sentiment and opinion.
- Implementing chatbots that can answer frequently asked questions or orchestrate predictable conversational dialogs that don't require the complexity of generative AI.