The Chatbots work based on three classification methods:
1.Pattern Matches: 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 simple example of Pattern matching is;
Then the machine gives the following output:
Human: Who invented the email?
Robot: According to Google, Ray Tomlinson invented email.
The Chatbot knows the appropriate answer because her or his name is in the related pattern. Similarly, the chatbots react to anything relating it to the correlate patterns. But it can’t go past the related pattern. To take it to a progressive stage, algorithms can help.
For every sort of question, a remarkable pattern must be accessible in the database to give a reasonable response. With a number of pattern combinations, it makes a hierarchical structure. We utilize algorithms to lessen the classifiers and produce the more reasonable structure.
2. Natural Language Understanding (NLU)This NLU has 3 specific concepts as follows:
Entities: This essentially represents an idea to your chatbot. For example, it may be a payment system in your E-commerce chatbot.
Context: When a natural language understanding algorithm examines a sentence, it doesn’t have the historical backdrop of the user’s text conversation. This implies that, if it gets a response to a question it has been recently asked, it won’t recall the inquiry. So, the phases during the conversation of chat are separately stored. It can either be banners like “Ordering Pizza”. Or could include other parameters like “Domino’s: Restaurant”. With context, you can easily relate expectations with the necessity of comprehending the last question.
Expectations: This is what a chatbot must fulfill when the customer says sends an inquiry. Which can be the same for different inquiries. For example, the goal triggered for, “I want to purchase a white pair of shoes”, and “Do you have white shoes? I want to purchase them” or “show me a white pair of shoes”, is the same: a list of shops selling white shoes. Hence, all user typing text show a single command which is the identifying tag; white shoes.
3. Natural Language Processing (NLP)
(NLP) Natural Language Processing Chatbots finds a way to convert the user’s speech or text into structured data. Which is then utilized to choose a relevant answer. Natural Language Processing includes the following steps;
- Tokenization: The NLP separates a series of words into tokens or pieces that are linguistically representative, with a different value in the application.
- Sentiment Analysis: It will study and learn the user’s experience, and transfer the inquiry to a human when necessary
- Normalization: This program model processes the text to find out the typographical errors and common spelling mistakes that might alter the intended meaning of the user’s request.
- Named Entity Recognition: The program model of chatbot looks for different categories of words, similar to the name of the particular product, the user’s address or name, whichever information is required.
- Dependency Parsing: The Chatbot searches for the subjects, verbs, objects, common phrases and nouns in the user’s text to discover related phrases that what users want to convey.