Natural Language Processing NLP

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Bots use pattern matches to group text to select the appropriate response. Artificial Intelligence Markup Language (AIML) is a standard structured model of these patterns. The bot can get the correct answer by comparing the request to the pattern. This matching requires many pre-generated patterns.

Natural Language Understanding (NLU)
NLU (Natural language understanding) is the ability of a chatbot to understand humans. It is the process of converting text into structured data for machine understanding. NLU follows three specific concepts: entities, context, and expectations.

Chatbot: Natural Language Understanding NLU

Entities are the idea behind the chatbot itself. For example, this could be a refund system in your e-commerce chatbot.
Context is a natural language recognition algorithm that identifies the query. The context of the query will prompt the bot to the correct answer.
Expectations – The bot should be able to meet the expectations of customers when they enter a query.
Natural Language Processing (NLP)
Natural language processing (NLP) bots are designed to convert user text or speech into structured data. The data is then used to select the appropriate response.


Natural Language Processing (NLP) consists of the following steps:

Tokenization – NLP filters a set of gambling data brazil phone number into tokens.
Mood analysis – the bot interprets users' responses according to their emotions.
Normalization – checks for patterns (aka templates) that can change the meaning of a user's query.
Entity recognition – the bot searches for different categories of required information.
Dependency analysis – the chatbot looks for common phrases that users might use.
What types of chatbots are there?
Chatbots process data to quickly respond to any user queries using pre-defined rules using AI. There are two types of chatbots.

Rule-based chatbots
They follow pre-defined paths during a conversation. At each step during a conversation, the user will need to select one of the options that determine the next step in the conversation.

Key Features:

Such bots follow predefined rules, making it easier to use for simpler scenarios.
Rule-based interactions with chatbots are clearly structured and are most applicable to customer support functions.
Rules-based bots are ideal for answering common queries, such as business hours, delivery status, or tracking details.