# How do we analyze user input? **Natural language understanding** (NLU) is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension. Language understanding is a fairly complicated task as it involves understanding the meaning of the sentence, extracting the entities, and making a decision regarding the action to be taken. The NLU's ability to understand the user relies on **training of knowledge domains.** This means that we can expand NLU capability to understand new domains by adding a new knowledge domain for it to train on. The main goal is to **understand user textual input and convert it into structured data** that holds, among other things, the extracted entities, an action to be taken, and a textual response for the user. ## **How do we classify?** The main tasks we perform in the classification node are Entity extraction and Intent classification: 1. **Entity extraction** - extract interesting parts of the text like names and locations. 2. **Intent classification** - understand the intention of the text and classify it into predefined classes (each intent represents a class). Our classification pipeline consists of a few steps: #### **Text Preprocessing** Use advanced techniques to normalize the text: perform text lemmatization - tag the Part of Speech (noun, verb, adjective, etc.) to find the important parts of the text, remove stop-words (words such “a”, “an” “the”). #### **Vectorization** Represent the text by numbers. We use a semantic representation, meaning word synonyms have an identical representation. #### **Classification** Use a machine learning fine-tuned classification algorithm to get the final classification label. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://studio.docs.ai.vonage.com/properties-1/intents/the-meaning-of-nlu.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.