Developers can train the bot on multiple interactions and conversations it will go through as well as providing multiple examples of content it will come in contact with as that will make the bot better in assessing and interpreting queries more effectively. This can be achieved by training the chatbots rigorously. In the example above, the behaviour exhibited by the bot is quite similar to how a human operator would have handled the query.
Contextual chatbot examples android#
In the context of bots, it helps in assessing the intent of the input from the users and then creates responses based on a contextual analysis similar to the way humans would have done.įor example, a customer on an eCommerce site types in “ Looking for 128 GB RAM android smartphone.” Since the bot is trained and recognizes the terms from its product database, it brings back a list with all the smartphones meeting these criteria from its database. Natural Language Processing or NLP as it is popularly called, refers to the branch of artificial intelligence or AI which deals with the ability of computers to understand the text and spoken words in much the same way human beings can. Infographic on Intents and entities in NLP Chatbot:īefore we get into the difference between intents and entities, let us first understand what Natural Language Processing is. And, the science that helps in extracting this relevant information is known as NLP (Natural language processing) – the foundation on which smart chatbots are built. To better understand what is happening in a sentence, we need to know all the intents and their corresponding entities.
The more entities an intent can identify, the more robust it is. Intents and entities are the building blocks of natural language understanding by context.
Last Updated on FebruThe Role of Intent and Entities in Understanding Context in Natural Language Processing Systems
0 kommentar(er)
