Chatbots are an ‘automatic kind of human’ to computer communication and understanding system which works with speech or text. They are often operational 24/7. It is supposed to be designed to manage millions of requests simultaneously and is applied mainly for conversion purposes.
Natural language processing brings into operation a type of smart, social, and interactive software program denoted as chatbots. They can perform, analyze and comprehend command.
Since chatbots have been introduced in the market for a while, more open source programming frameworks, numerical data availability and Processing power have all contributed for it.
That is why the application of chatbots has become widespread in various industries and spheres. The use of chatbots is wide spread and may be observed in customer support, social networks, web-stores, and banking systems.
Chatbots are built to do specific tasks. For instance, the conversational chatbots are intended for conversing with the users and the customer care chatbots are particularly developed for catering to demanding customers who seek assistance.
The creation of a powerful chatbot and its ability to achieve human standard interaction is possible with a collection of large data set but before it is launched into the market, it must undergo business analysis and quality assurance test.
But when a chatbot engages in repetitive performing of operations with regard to pre-determined variables as well as specific machine learning algorithms behind AI chatbots, it dispenses human-like voice. A bot is built to communicate with a human using voice messages or a chat on a mobile/ web application in a similar way that the user does. Chatbots are one of the forms of conversational AI that in some way resemble virtual assistants.
This is perhaps the simplest type of chatbot which is a rules-based software that comes up with an answer after following a procedure that is sequential in a tree form. These ones are not so much of artificial intelligence , though they are called AI, and they respond to certain sets of queries by using pattern matching and a knowledge base to respond with already typed messages.
However, when an AI code is integrated with the chat application, then this bot has greater intelligence, and seems more like a real human. Chatbots that are infused with artificial intelligence employ deep learning, machine learning, natural language processing, and pattern matching.
There are different techniques used in machine learning algorithms behind AI chatbots. Among them, the most often used type is machine learning algorithms behind AI chatbots based on the methods of natural language processing. Text operations, categorisation, and analysis are significant for the creation of high-quality chatbots when they are designed to accept natural language input.
Naive Bayes algorithm: The e Bayes algorithm tries to categorize the text so as to allow the chatbot to determine the specific intention of the user, thereby reducing the field of possible responses. As intent recognition remains one of the first steps, and a quite vital one in the course of the conversation with the chatbot.
It is crucial for this algorithm to work as planned. Some of the terms pertaining to certain categories should be assigned a higher weight within that category since the method uses frequency as its base. That allows for classification of purpose and phrasing of textual data.
Support vector machine: It needs to be pointed out that SVMs operate based on the Structural Risk Minimization Principle. SVMs produce outstanding results when used with text data and Chatbots due to enormous dimensional inputs from features of text quantity, linearly separable data, and the use of sparse matrices.
Another is an algorithm that can commonly be used for the categorization of documents and determining their functions and hence, it is well-liked.
Algorithms for natural language processing : Of these two components, NLP plays a crucial role for chatbots, as it defines how the bot will be able to process and comprehend the text entered in the chat. Such a perfect chatbot should almost go unnoticed by the consumer and the consumer would almost not realize that he or she is in fact interacting with a machine.
This program tries to incorporate the essence of the human language by utilizing machine learning algorithms behind AI chatbots and large data from typical conversations. Text mining is useful for the bot in the meantime because it allows it to parse grammatical structures, affective tones, and the text’s primary purpose.
This is so because NLP has many functionalities like the sentiment polarity, word vectors, the topic modeling, PoS tagging, n-gram, and text summary.