When you contact a customer service chatbot and type "where is my package?", the bot uses NLU to determine that your intention is to check the delivery status.
Extracts the necessary entity - your order information - and provides the correct update. This ability to understand and respond to various customer queries is what makes nlu an essential part of modern customer service automation.
Email classification and automation
the nlu is also behind email norway mobile phone number automation systems. For example, nlu-powered tools can read incoming emails, understand the content, and automatically classify them into categories like “urgent,” “promotions,” or “meetings.”
These systems can even generate appropriate responses based on the content of the email, saving companies time in managing communication.
Text analysis for comments and surveys
NLU is often used by companies to analyze survey comments, reviews, and social media posts.
Nlu helps identify patterns and feelings in written language, allowing you to understand customer needs and opinions.
For example, an nlu system can scan hundreds of customer reviews and determine whether the majority of users have a positive or negative opinion about a specific feature using sentiment analysis.
Key components
blue and gray lines in an abstract cubic pattern.
Tokenization
Tokenization is the process of breaking down a phrase into smaller units, such as words or phrases, to make it easier for AI to process.
Example: "schedule a meeting for tomorrow at 15:00" is tokenized to ["schedule", "a", "meeting", "for", "15:00", "tomorrow"].
Part-of-speech (pos) tagging
POS tagging identifies the grammatical structure of a sentence by labeling each word as a noun, verb, adjective, etc.
Example: In "schedule a meeting," the AI labels "schedule" as a verb and "meeting" as a noun.
Named Entity Recognition (NER)
Named Entity Recognition (NER) detects and classifies important entities such as names, places, and dates within text.
Example: in "book a flight to new york next friday", the AI identifies "new york" as the location and "next friday" as the date.
Intent Classification
Intent classification determines the user's underlying goal or purpose behind their input.
Example: "reserve a table for two" is classified as the intention to make a reservation.
