The most crucial duty of a chatbot is to analyse and determine the purpose of the user's request in order to extract relevant entities. After the analysis is completed, the user receives the proper response.
Bots utilize pattern matches to group the text and it produces an appropriate response from the clients. Artificial Intelligence Markup Language (AIML) is a standard structured model of these patterns. A bot is able to get the right answer in the related pattern. The bots react to anything relating to the correlated patterns.
Natural language understanding (NLU)
Bots use pattern matching to group text, prompting users to answer appropriately. Artificial
Intelligence Markup Language is a popular structured model of these patterns (AIML). A bot can
respond in the appropriate manner. The bots respond whenever one of the linked patterns is
mentioned.
Natural language processing (NLP)
Bots that use natural language processing (NLP) are made to turn user-provided text or audio inputs
into structured data. The information is then used to select an appropriate response. Tokenization,
sentiment analysis for chatbots, entity recognition, and dependency parsing are a few crucial NLP
tasks.
Rule-based chatbots, also known as decision-tree bots, operate according to a set of predetermined rules. The types of issues the chatbot is familiar with and capable of solving are based on these rules.