> For the complete documentation index, see [llms.txt](https://studio.docs.ai.vonage.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://studio.docs.ai.vonage.com/properties-1/intents/the-meaning-of-nlu.md). # How do we analyze user input? Natural Language Understanding **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 This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://studio.docs.ai.vonage.com/properties-1/intents/the-meaning-of-nlu.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.