How do chatbots work? A guide to chatbot architecture-Explained by Digimagg

It's widely acknowledged that chatbots have been part of the digital landscape for some time now. Initial doubts about their usefulness have largely disappeared, with chatbots now considered essential for businesses of all sizes looking to streamline customer support and automate lead generation.

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What Is A Chatbot?

A chatbot is a program designed to engage in conversation with humans. Users can ask questions or make statements, and the bot will respond accordingly, much like texting with a friend. It simulates human conversation and can communicate through messages or voice commands. Chatbots operate independently, without human intervention, and respond to queries using a combination of predefined scripts and machine learning.

When a chatbot encounters a question, it uses its existing knowledge base to provide an answer. If the conversation introduces a new concept, the chatbot may transfer the conversation to a human operator. Through these interactions, the chatbot learns and improves over time, expanding its capabilities and relevance.

How Do Chatbots Work?

Bots serve specific purposes. For example, a store might use a chatbot to help customers place orders, while a telecom company might create one to handle customer service inquiries.

There are two types of chatbots: rule-based, which follow predetermined instructions, and AI-based, which use artificial intelligence.

 AI-based chatbots

This chatbot is powered by artificial intelligence (AI), which enables it to understand and respond to open-ended queries. It learns from user interactions, improving its ability to comprehend language and respond appropriately. The AI chatbot can identify language, context, and intent, allowing it to provide more accurate responses over time.

 Rule-based chatbots

A rule-based chatbot is limited to the predefined choices programmed into it. It follows a set of rules to guide its conversations and responses. These chatbots are relatively easy to create, using a straightforward true-false algorithm to understand user queries and provide appropriate answers.

What Is Chatbot Architecture?

The architecture of a chatbot serves as its backbone, and the specific type of architecture depends on factors such as the use case, domain, and type of chatbot. Despite these variations, the fundamental flow of conversation remains consistent. Let's delve into the essential components of chatbot architecture:

Environment

"The environment plays a crucial role in contextualizing users' messages through natural language processing (NLP).

The NLP Engine is the central component of chatbot architecture, responsible for interpreting user messages and converting them into structured inputs for the system. It utilizes advanced machine learning algorithms to identify the user's intent and match it with the bot's list of supported intents.

The NLP Engine comprises two key components:

1. Intent Classifier: This component maps user queries to the actions to be performed by the software.
2. Entity Extractor: The entity extractor identifies keywords in the user's query to determine their specific request or information.

The NLP engine can be further enhanced with a feedback mechanism and policy learning for improved learning:

Feedback Mechanism: Users can provide feedback to the chatbot at the end of a conversation, enabling it to learn from its mistakes and enhance future interactions.
Policy Learning: This framework trains the bot to establish a network of successful conversation paths, enhancing overall user satisfaction."

Question and Answer System

The Q&A system is designed to address frequently asked questions from customers. It interprets questions and provides relevant answers from its knowledge base. It consists of two main elements:

1. Manual Training: This involves domain specialists compiling a list of common user questions and their corresponding answers. This helps the chatbot quickly identify relevant answers to common questions.

2. Automated Training: This involves providing the chatbot with business documents, such as policy documents, to train itself. The chatbot generates a list of questions and answers from these documents, enabling it to respond confidently to user queries.

Node Server / Traffic Server

The server manages user traffic requests and directs them to the appropriate components. Responses from internal components are often sent through the traffic server to the front-end systems.

 Front-End Systems

Front-end systems are where users engage with the chatbot. These systems, such as Facebook Messenger, WhatsApp Business, Slack, Google Hangouts, your website, or mobile app, are client-facing.

Custom Integrations

Custom integrations allow your chatbot to connect with your current backend systems, such as CRM, databases, payment applications, calendars, and other tools. This integration enhances your chatbot's functionality.

How Do Chatbots Work?

Chatbots typically adopt three classification models to function:

Algorithms

A database must contain unique patterns to provide suitable responses for different types of questions, creating a hierarchical structure with numerous pattern combinations. Algorithms are used to streamline the classifiers and create a more manageable system. This approach, known as the "Reductionist" approach, simplifies the problem.

The Multinational Naive Bayes algorithm is a prime example of an algorithm used for natural language processing (NLP) and text classification. For example, consider a set of sentences belonging to a specific class. With new input sentences, each word's occurrence is counted and its commonality is noted. Then, each class is given a score, with the highest score indicating the most likely association with the input sentence.

Pattern Matchers

Bots utilize pattern matching to categorize text and generate appropriate responses for customers. A common framework for these patterns is the "Artificial Intelligence Markup Language" (AIML).

Here's a straightforward example of pattern matching:

Artificial Neural Networks

Neural Networks are a method for calculating output from input by using weighted connections, which are adjusted through repeated iterations during training. Each iteration with the training data modifies the weights, leading to more accurate output.

As mentioned earlier, each sentence is divided into individual words, and these words serve as inputs for the neural network. The weighted connections are then computed through numerous iterations over the training data, with each iteration refining the weights for improved accuracy.

The trained data of a neural network can be represented as a comparative algorithm with more or less code. For instance, with a relatively small sample where the training sentences contain 200 different words and 20 classes, the matrix size would be 200×20. However, this matrix size gradually increases with more data, leading to a large number of computations. In such cases, a high processing speed is essential.

Neural networks come in various forms, with different algorithms and patterns matching code. This can add complexity, but the underlying principle remains the same, with the primary focus on classification.

What Is NLU (NATURAL LANGUAGE UNDERSTANDING)?

NLU, or Natural Language Understanding, assists the chatbot in comprehending user queries by breaking them down into three key concepts:

1. Intents: Intents help determine the action the chatbot should take based on the user's input. For instance, the intents of "I want to order a t-shirt", "Do you have a t-shirt? I want to order one", and "Show me some t-shirts" are the same, as they all trigger a command related to t-shirts.
2. Entities: These are keywords or phrases in the user's query that the chatbot identifies to understand the user's intent. For example, in the query "What is my outstanding bill?", the word "bill" is an entity.
3. Context: Understanding the context of a conversation can be challenging for an NLU algorithm without access to the conversation history. To differentiate between different phases of a chat conversation, the algorithm's state must be stored. This can include flagging phrases like "Ordering Pizza" or parameters like "Restaurant: 'Dominos'". With context, the chatbot can relate intents without needing to know the previous question.

What Is NLP (NATURAL LANGUAGE PROCESSING)?

A Natural Language Processing (NLP) chatbot undergoes several steps to convert a customer's text or speech into structured data to select the appropriate response. Some of the key NLP steps include:

• Tokenization: NLP breaks down a string of words into individual tokens. These tokens are linguistically meaningful and help the application understand the text.

• Named Entity Recognition: The chatbot's model identifies categories of words, such as product names, user names, or addresses, as needed for the conversation.

• Sentiment Analysis: This step involves the algorithm attempting to understand the sentiment behind the user's query by analyzing the entities, themes, and topics.
• Dependency Parsing: The chatbot analyzes the user's text to identify objects, subjects, verbs, nouns, and common phrases, understanding the dependencies and relationships between terms.
• Normalization: The chatbot processes the text to correct common spelling mistakes or typographical errors, making its responses more human-like.

Just like other applications, the chatbot is linked to a database containing a knowledge base. This database provides the chatbot with the information it needs to respond appropriately to users.

The data store captures information about whether the chatbot can match users' questions. NLP translates human language into patterns and text that can be mapped in real-time to find suitable responses.

How Can Chatbots Improve Sales, Marketing, and Customer Service?

Chatbots streamline various functions for companies by automating tasks. They aid in lead generation and communication with clients, simplifying the process. By asking qualifying questions, chatbots can assess leads and help the sales team prioritize their efforts.

In customer support, chatbots provide instant answers, reducing costs. They can also escalate complex queries to human agents. Chatbots simplify order management and notification processes. Their interactive nature enables personalized customer experiences. Learn more in our comprehensive chatbot guide.

What are the benefits of chatbots?

• Chatbots are gaining significance due to their financial advantages.
• They offer personalized connections to a vast number of users.
• Chatbots automate repetitive tasks.
• They enable superior customer service and tailored experiences.
• Chatbots are interactive, engaging in two-way conversations with recommendations.
• They are highly efficient.

To Sum Up

Today, most companies have an online presence through websites or social media. Utilizing custom chatbots allows businesses to easily communicate with their target audience. Advances in natural language processing enable chatbots to interact with consumers as humans do, saving resources, costs, and time.