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NLP Chatbot: Complete Guide & How to Build Your Own

by Kazimierz Rajnerowicz·Updated
nlp chatbot cover image

Self-service tools, conversational interfaces, and bot automations are all the rage right now. Businesses love them because chatbots increase engagement and reduce operational costs.

Some might say, though, that chatbots have many limitations, and they definitely can’t carry a conversation the way a human can.


NLP chatbots, the ones that use natural language processing, are getting better and better. In fact, NLP is changing the game.

How do they work and how to bring your very own NLP chatbot to life? This is exactly what we’ll talk about.

In this article:

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If you are interested in learning more about chatbots, you might also want to read:


Let’s get started with the basics—

Do all chatbots use natural language processing? And if not, how are NLP chatbots different from traditional chatbots?

What is natural language processing for chatbots?

NLP chatbots are powered by natural language processing (NLP) technology, a branch of artificial intelligence that deals with understanding human language. It allows chatbots to interpret the user’s intent and respond accordingly.

Traditional chatbots, on the other hand, are powered by simple pattern matching. They rely on predetermined rules and keywords to interpret the user’s input and provide a response. This approach is often limited and can lead to errors.

For example, if we asked a traditional chatbot, “What is the weather like today?” it would be able to recognize the word “weather” and send a pre-programmed response. But if we asked “What is the best song by Weather Report?” we would probably get the same response. The rule-based chatbot wouldn’t be able to understand the user’s intent.

That can be a problem. About 74% of users do prefer chatbots to customer service agents when seeking answers to simple questions. However, chatbot technology most of the time is not sophisticated enough to handle complex or nuanced questions—

And that’s where the new generation of NLP-based chatbots comes into play.

Natural language processing chatbots are much more versatile and can handle nuanced questions with ease. By understanding the context and meaning of the user’s input, they can provide a more accurate and relevant response.

In our example, a GPT-3 chatbot (trained on millions of websites) was able to recognize that the user was actually asking for a song recommendation, not a weather report.

Read more: What Is the Difference Between Chatbots and Conversational AI?

Difference between NLP, NLU, and NLG

In short, chatbot NLP uses NLU, and NLG. But here are explanations and the differences between these processes:

Natural language understanding (NLU) is the process of a machine understanding the meaning of the text. It takes place when the system converts the user’s input into a form that’s logical for the computer’s algorithms.

Natural language generation (NLG) takes place in order for the machine to generate a logical response to the query it received from the user. It first creates the answer and then converts it into a language that a human can understand.

Natural language processing (NLP) combines these operations to understand the given input and answer appropriately. It combines NLU and NLG to enable communication between the user and the software.

How NLP works in chatbot apps

Natural language processing draws on many disciplines, including computer science, linguistics, psychology, mathematics, and statistics. It can be difficult to draw clear boundaries between these disciplines because they all contribute to NLP.

NLP algorithms for chatbot are designed to automatically process large amounts of natural language data. They’re typically based on statistical models, which learn to recognize patterns in the data. These models can be used by the chatbots NLP to perform various tasks, such as machine translation, sentiment analysis, speech recognition, and topic segmentation.

NLP processes includes:

  • Lexical analysis—identifying all the different words in a text and understanding their meaning
  • Syntactic analysis—analyzing the way that these words are put together to form phrases and sentences
  • Semantic analysis—determining the relationships between words and concepts
  • Pragmatic analysis—understanding how language is used in different situations

In terms of the learning algorithms and processes involved, language-learning chatbots generally rely heavily on machine-learning methods, especially statistical methods. They allow computers to analyze the rules governing the structure and meaning of language from data. Apps such as voice assistants and NLP-based chatbots can then use these language rules to process and generate utterances of a conversation.

What does the chatbot NLP training look like?

First, NLP conversational AI is trained on a data set of human-to-human conversations. Then, this data set is used to develop a model of how humans communicate. Finally, the system uses this model to interpret the user’s utterances and respond in a way that is natural and human-like.

There are many techniques and resources that you can use to train a chatbot. Many of the best chatbot NLP models are trained on websites and open databases. You can also use text mining to extract information from unstructured data, such as online customer reviews or social media posts.

Read more: Check out the Lyro case study to learn how this AI customer service chatbot handled the majority of support questions.

Why use NLP chatbots?

The chatbot market is projected to reach over $100 billion by 2026. And that’s understandable when you consider that NLP for chatbots can improve your business communication with customers and the overall satisfaction of your shoppers. 

In fact, this technology can solve two of the most frustrating aspects of customer service, namely having to repeat yourself and being put on hold.

Chatbots that use NLP technology can understand your visitors better and answer questions in a matter of seconds. On average, chatbots can solve about 70% of all your customer queries. This helps you keep your audience engaged and happy, which can increase your sales in the long run.

Now that you know the basics of AI NLP chatbots, let’s take a look at how you can build one.

Different methods to build a chatbot using NLP

There are several different methods to approach this. But, ultimately, your choices boil down to:

  1. Developing an NLP chatbot from scratch
  2. Using an existing chatbot framework NLP
  3. Designing bots with an NLP chatbot platform

Let’s take a look at each of the methods of how to build a chatbot using NLP in more detail.

1. NLP chatbot in Python

If you decide to create your own NLP AI chatbot from scratch, you’ll need to have a strong understanding of coding both artificial intelligence and natural language processing. This option is recommended for experienced developers only.

nltk for phyton example

The most common way to do this would be coding a chatbot in Python with the use of NLP libraries such as Natural Language Toolkit (NLTK) or spaCy. This method is the most complex and time-consuming. Unless you are a software developer specializing in chatbots and AI, you should consider one of the other methods listed below.

2. Chatbot frameworks with NLP engines

Some of you probably don’t want to reinvent the wheel and mostly just want something that works. In this case, you can use an existing chatbot framework. Thankfully, there are plenty of open-source NLP chatbot options available online.

Microsoft Bot Framework app architecture

The most popular choices include Microsoft Bot Framework, Amazon Lex, IBM, and Google Dialogflow. These frameworks will give you the building blocks you need to develop a chatbot. However, you’ll still need to put in a lot of work and know how to code to use these chatbot NLP architectures effectively.

3. NLP chatbot platforms

If you want to avoid the hassle of developing and maintaining your own NLP chatbot, you can use an NLP chatbot platform. These ready-to-use chatbot apps provide everything you need to create and deploy a chatbot, without any coding required.

Chatbot-building tools available in Tidio

All you need to do is set up separate bot workflows for different user intents based on common requests. These platforms have some of the easiest and best NLP engines for bots. From the user’s perspective, they just need to type or say something, and the NLP support chatbot will know how to respond.

Set up your own AI chatbot with NLP functionalities in minutes

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The setup process is relatively fast and easy. For example, adding a new chatbot to your website or social media with Tidio takes only several minutes. A few of the best NLP chatbot examples include Lyro by Tidio, ChatGPT, and Intercom.

Read more: Discover the most popular types of chatbots available on the market. Also, learn what the best AI-powered chatbots are for your business.

Now, here’s how to set up our own NLP bot with the chatbot builder.

How to make a natural language processing chatbot

To show you how easy it is to create an NLP conversational chatbot, we’ll use Tidio. It’s a visual drag-and-drop builder with support for natural language processing and intent recognition. You don’t need any coding skills to use it—just some basic knowledge of how chatbots work.

If you want to follow along, sign up for a free Tidio account now.

1. Set up your account and customize the widget

setting up tidio chatbot image

When you first log in to Tidio, you’ll be asked to set up your account and customize the chat widget. The widget is what your users will interact with when they talk to your chatbot. You can choose from a variety of colors and styles to match your brand.

Read more: Discover the best chat widgets for your website.

2. Train your chatbot with popular customer queries

example of chatbot training

Now it’s time for chatbot NLP training. You can add as many synonyms and variations of each query as you like. Just remember, each Visitor Says node that begins the conversation flow of a bot should focus on one type of user intent.

The editing panel of your individual Visitor Says nodes is where you’ll teach NLP to understand customer queries. The app makes it easy with ready-made query suggestions based on popular customer support requests. You can even switch between different languages and use a chatbot with NLP in English, French, Spanish, and other languages.

If you’re using the newest conversational AI, Lyro, you can skip training as the software trains itself on the data from your website. Check out this tutorial on how to easily set up Lyro:

Read more: Discover the most popular customer database solutions available on the market.

3. Design conversation trees and bot behavior

example of conversation tree in Tidio

To design the conversation flows and chatbot behavior, you’ll need to create a diagram. It will show how the chatbot should respond to different user inputs and actions. You can use the drag-and-drop blocks to create custom conversation trees. Some blocks can randomize the chatbot’s response, make the chat more interactive, or send the user to a human agent.

If you don’t want to write appropriate responses on your own, you can pick one of the available chatbot templates.

There is also a wide range of integrations available, so you can connect your chatbot to the tools you already use, for instance through a Send to Zapier node, JavaScript API, or native integrations.

Read more: Check out best practices and tips for chatbot design with examples.

4. Monitor your results to improve customer experience

results monitoring panel image

Last but not least, Tidio provides comprehensive analytics to help you monitor your chatbot’s performance and customer satisfaction. For instance, you can see the engagement rates, how many users found the chatbot helpful, or how many queries your bot couldn’t answer.

These insights are extremely useful for improving your chatbot designs, adding new features, or making changes to the conversation flows.

Read more: Learn the essential chatbot metrics that your business should track.

As you can see, setting up your own NLP chatbots is relatively easy if you allow a chatbot service to do all the heavy lifting for you. You don’t need any coding skills or artificial intelligence expertise. In case you need more help, you can always reach out to the Tidio team or read our detailed guide on how to build a chatbot.

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The challenges of NLP chatbot development

NLP chatbots are still a relatively new technology, which means there’s a lot of potential for growth and development. Here are a few things to keep in mind as you get started with natural language bots.

  • They’re not perfect

NLP bots are powered by artificial intelligence, which means they’re not perfect. They may make mistakes or misinterpret what you’re saying. However, as this technology continues to develop, AI chatbots will become more and more accurate.

  • They need to be trained

Just like any other artificial intelligence technology, natural language processing in chatbots need to be trained. This involves feeding them a large amount of data, so they can learn how to interpret human language. The more data you give them, the better they’ll become at understanding natural language.

  • They’re not cheap

Chatbot and NLP technology can be expensive to develop and maintain. However, as the technology matures, the costs will likely come down. If you’re looking to create an NLP chatbot on a budget, you may want to consider using a pre-trained model or one of the popular chatbot platforms. It greatly reduces the average cost of developing a chatbot.

  • They need a user interface

Natural language chatbots need a user-friendly interface, so people can interact with them. This can be a simple text-based interface, or it can be a more complex graphical interface. It all depends on your needs and preferences. But designing a good chatbot UI can be as important as managing the NLP and setting up your conversation flows.

  • They require regular maintenance

Chatbots, like any other software, need to be regularly maintained to provide a good user experience. This includes adding new content, fixing bugs, and keeping the chatbot up-to-date with the latest changes in your domain. Depending on the size and complexity of your chatbot, this can amount to a significant amount of work.

Still, all of these challenges are worthwhile once you see your NLP chatbot in action, delivering results for your business. Just keep the above-mentioned aspects in mind, so you can set realistic expectations for your chatbot project.

Key takeaways

Natural language processing can be a powerful tool for chatbots, helping them to understand customer queries and respond accordingly. A good NLP engine can make all the difference between a self-service chatbot that offers a great customer experience and one that frustrates your customers.

The process of developing an NLP chatbot can vary in complexity depending on the programming language, bot framework, or online chatbot service you use:

  • Advanced developers and deep learning enthusiasts can try to incorporate NLP in a chatbot in Python by using dedicated libraries and modules
  • Those who want to speed up the process but still maintain full control over their bots can use existing chatbot frameworks such as Google Dialogflow
  • Business owners and beginners who need out-of-the-box solutions can use online chatbot tools that provide NLP features

If you want to create a chatbot without having to code, you can use a chatbot builder. Many of them offer an intuitive drag-and-drop interface, NLP support, and ready-made conversation flows. You can also connect a chatbot to your existing tech stack and messaging channels.

Are you ready to get started with NLP chatbots? You can create your free account now and start building your chatbot right off the bat.

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Frequently asked questions

What is an NLP chatbot?

An NLP chatbot is a virtual agent that understands and responds to human language messages. It, most often, uses a combination of NLU, NLG, artificial intelligence, and machine learning to convert human language into something it can understand and then generate a response that’s understandable to humans.

How to build an NLP chatbot?

The easiest way to build an NLP chatbot is to sign up to a platform that offers chatbots and natural language processing technology. Then, give the bots a dataset for each intent to train the software and add them to your website. These NLP chatbots will learn more and more with time.

What is the difference between NLP and chatbots?

The difference between NLP and chatbots is that natural language processing is one of the components that is used in chatbots. NLP is the technology that allows bots to communicate with people using natural language.

What is the use of an NLP chatbot in the real world?

Natural language processing chatbots are used in customer service tools, virtual assistants, etc. Some real-world use cases include customer service, marketing, and sales, as well as chatting, medical checks, and banking purposes.

Is NLP different from AI?

NLP is part of AI technology. Artificial intelligence tools use natural language processing to understand the input of the user.

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Kazimierz Rajnerowicz
Kazimierz Rajnerowicz

Kazimierz was one of the original authors of Tidio Blog. His primary objective was to deliver high-quality content that was actionable and fun to read. His interests revolved around AI technology and chatbot development.

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