Using the Chatbot software machines can be used to communicate with humans in a natural, conversational manner. Chatbots have numerous uses in different industries. From answering FAQs, communicating with customers, and providing better insights about customers’ needs.
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But before that, it is important to understand the underlying architecture of chatbots to reap the most of their benefits.
Let’s explore how chatbots work, their components, and the steps involved in chatbot architecture.
The working of Chatbots
Chatbots aim to understand users’ queries and help in generating a relevant response that meets users’ needs. Simple chatbots are used to scan users’ input sentences for general keywords, skim through their predefined list of answers, and provide a response relevant to the user’s query.
Much more modern chatbots rely on AI and natural language processing (NLP). It recognizes users’ intent from the context of their input and generates correct responses.
There are 3 types of Chatbots based on the response-generation method
- AI-based chatbots
To determine the right way to respond, AI-enabled chatbots make use of NLP to scan users’ queries and recognize keywords. Some AI-based chatbots also self-improve by using users’ data as new training data to expand the knowledge database and improve their responses.
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- Rule-based chatbots
Rule-based chatbots rely on if/then logic. It generates responses based on predefined conditions and responses and has limited customization capabilities. But these chatbots are reliable and are less likely to go off the rails.
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- Hybrid chatbots
To understand users and generate responses, Hybrid chatbots rely both on rules and NLP. These chatbots’ databases are easier to tweak though they have limited conversational capabilities compared to AI-based chatbots.
The components of a chatbot?
Chatbots consists of 7 components and are structured as follows:
Natural language processing
With Natural language processing (NLP), chatbots convert users’ text and speech into structured data that can be understood by a machine. NLP consists of the following steps:
- Tokenization: also known as lexical analysis, is the process of splitting the string of words forming a sentence into smaller parts or “tokens” based on its meaning and their relationship to the whole sentence.
- Normalization: otherwise known as syntactic analysis, is the process of checking words for typos and changing them into the standard form. For example, the word “tmrw” will be normalized into “tomorrow.”
- Entity recognition: this is the process of looking for keywords to identify the topic of the conversation.
- Semantic analysis: this includes inferring the meaning of a sentence by understanding the meaning of each word and its relation to the overall structure.
Natural language understanding
Natural language understanding (NLU) is a subfield of NLP. This focuses on understanding the meaning of human speech by recognizing patterns in the unstructured speech input. NLU solutions have 3 components:
- Dictionary to determine the meaning of a word
- Parser to determine if the syntax of the text conforms to the rules of the language
- Grammar rules that are used to break down the input based on sentence structure and punctuation
With NLU, chatbots can classify users’ intents and generate a response based on training data.
This includes a library of information that the chatbot relies on to fetch the data used to respond to users. Based on business needs, knowledge bases differ. For instance, the knowledge base of an e-commerce website chatbot will contain information about products, features, and prices. But a knowledge base of a healthcare chatbot will have information about physicians’ calendars, hospital opening hours, and pharmacy duties.
Some chatbots are also integrated with web scrapers to pull data from online resources and display it to users.
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Chatbot developers store conversations for customer service use and bot training and testing purposes. A Chatbot conversation can be stored in SQL form either on-premise or on a cloud.
A dialog manager is a component that manages the flow of the conversation between the user and the chatbot. It keeps a record of the interactions within one conversation to decide how to respond.
For instance, if the user says “I want to order strawberry ice cream” and then within the conversation says “change my order to chocolate ice cream”, the dialog manager will enable the bot to detect the change from “strawberry” to “chocolate” and change the order accordingly.
Natural language generation
The process of transforming machine-produced structured data into human-readable text is known as Natural language generation (NLG). After understanding users’ intent, NLG generates the following as a response:
- Content determination: This includes, filtering of the existing data in the knowledge base that enables to choose what to include in the response.
- Data interpretation: Related to understanding the patterns and answers available in the knowledge base.
- Document planning: Method of structuring the answer narratively.
- Sentence aggregation: Comprises of compiling the expressions and words for each sentence in the response.
- Grammaticalization: Application of grammar rules such as punctuation and spell-check.
- Language implementations: The method of inputting the data into language templates to ensure a natural representation of the response.
Conversational user interfaces are the front-end of a chatbot that enables the physical representation of the conversation. It is classified into text-based or voice-based assistants and can be integrated into different platforms, such as Facebook Messenger, WhatsApp, Slack, Google Teams, etc.
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Include the best practices of the chatbot development process to identify the target audience and understand their needs. This is essential in setting realistic goals for chatbot implementation
Following this, you need to understand which business area will benefit most from a chatbot and then select the right chatbot vendor.
Our web chatbot agency in San Jose will help you optimize the usability and the accessibility of your chatbot on the customer side of the chatbot.