How to Build a Chatbot using Natural Language Processing?

Chatbots: History, technology, and applications

chatbot using natural language processing

The answer to these questions is natural language processing (NLP). 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.

chatbot using natural language processing

The AI-based chatbot can learn from every interaction and expand their knowledge. 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.


These insights are extremely useful for improving your chatbot designs, adding new features, or making changes to the conversation flows. Now, here’s how to set up our own NLP bot with the chatbot builder. Some of you probably don’t want to reinvent the wheel and mostly just want something that works.

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By following this article’s explanation of ChatBots, their utility in business, and how to implement them, we may create a primitive Chatbot using Python and the Chatterbot Library. Anyone interested in gaining a better knowledge of conversational artificial intelligence will benefit greatly from this article. Therapeutic chatbot that distributes the text into labels for emotions happiness, pleasure, shame, rage, disgust, sorrow, remorse, and Afraid.

Why use NLP chatbots?

Having made your landing point in this way, now you need to set up an NLP model that the chatbot can access. The great news here is that we have pre-trained NLP models with some of the more important challenges facing chatbots in mind. These models have been trained with huge numbers of sentence examples and they are really effective. The Natural Language Toolkit (NLTK) is a platform used for building Python programs to work with human language data.

  • You will learn the basic methods and techniques of NLP using an awesome open-source library called spaCy.
  • After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning.
  • Natural Language Processing is the way in which computer software gets to grips with human conversation and analyses the meaning of sentences.
  • Chatbots are becoming increasingly popular as businesses seek to automate customer service and streamline interactions.
  • If a task can be accomplished in just a couple of clicks, making the user type it all up is most certainly not making things easier.
  • An NLP chatbot is a virtual agent that understands and responds to human language messages.

Some might say, though, that chatbots have many limitations, and they definitely can’t carry a conversation the way a human can. Before building a chatbot, it is important to understand the problem you are trying to solve. For example, you need to define the goal of the chatbot, who the target audience is, and what tasks the chatbot will be able to perform.

Beginner’s Guide to Building a Chatbot Using NLP

And these are just some of the benefits businesses will see with an NLP chatbot on their support team. 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.

chatbot using natural language processing

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. And that’s where the new generation of NLP-based chatbots comes into play. Once the chatbot is tested and evaluated, it is ready for deployment. This includes making the chatbot available to the target audience and setting up the necessary infrastructure to support the chatbot. Follow the steps below to build a conversational interface for our chatbot successfully.

Our Expertise in Chatbot Development

However, the big disadvantages is that these natural responses require a great amount of learning time and data to be able to learn the vast amount of possible inputs. The training will prove if the bots are able to handle the more challenging issues that are normally obstacles for simpler chatbots. 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. Natural language processing chatbots are much more versatile and can handle nuanced questions with ease.

For instance, the entities of the question, “What are your closing hours on Tuesday? An entity is basically anything that can be named (like place, person, name, or object). And that’s thanks to the implementation of Natural Language Processing into chatbot software. This is simple chatbot using NLP which is implemented on Flask WebApp. If you really want to feel safe, if the user isn’t getting the answers he or she wants, you can set up a trigger for human agent takeover.

He is also a speaker at PyLadies meetup group, ladies who code in Python which is led by one of the former director of PSF(Python Software Foundation). The innovation is that it is not only the answer generator but a clever interface to an answer generator that determines our method. In the future, a voice-based system is included, and using Tkinter GUI has developed the chatbot for better use. A chatbot can assist customers when they are choosing a movie to watch or a concert to attend.

  • This step will create an intents JSON file that lists all the possible outcomes of user interactions with our chatbot.
  • If you look at the simpler chatbots, any response (provided it was correct grammar beforehand) is void of any grammatical error.
  • Follow the steps below to build a conversational interface for our chatbot successfully.
  • Remember, if you need assistance with Python development, don’t hesitate to hire remote Python developers.
  • Just like how AI is an broad and enormous field, natural language processing is also essentially an ocean of different algorithms used to convert text to important data for the chatbot to use.
  • Natural language processing can greatly facilitate our everyday life and business.

You can choose the language you want the NLP model to work in and you’ll be given a warning that importing these models will overwrite any existing ones. This command will start the Rasa shell, and you can interact with your chatbot by typing messages. A way to extract the essential parts of a sentence is to differentiate between the entities and the intent. For instance, if the message was, “When does the Chipotle at 24th Street close? ” the intent of the message is to know when the restaurant closes. An entity of a sentence is something that modifies or supports the intent.

Find out more about NLP, the tech behind ChatGPT

Thankfully, there are plenty of open-source NLP chatbot options available online. This command will train the chatbot model and save it in the models/ directory. Now that we have installed the required libraries, let’s create a simple chatbot using Rasa.

chatbot using natural language processing

And this is because they use simple keywords or pattern matching — rather than using AI to understand a customer’s message in its entirety. 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.

chatbot using natural language processing

If you don’t want to write appropriate responses on your own, you can pick one of the available chatbot templates. Now that you know the basics of AI NLP chatbots, let’s take a look at how you can build one. 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. 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. You now have a powerful NLP system in place and your chatbot is suddenly capable of appraising a conversation — in this case for negative statements — and reacting accordingly. Click the Default Models button and you may as well avail of them all.

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Let’s suppose you have a scripted chatbot and it’s doing a good job in answering queries 24/7, perhaps as an SMS chatbot, conversing with someone by text. But you want to take it to a whole new level, because you know from the analytics that several users have broken off from the conversation in frustration. Good communication with your customers and clients is important to you. So you want to create a chatbot that is sensitive to the emotions being expressed by your chatbot user. Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that enables computers to understand, interpret, and generate human language. It involves the processing and analysis of text to extract insights, generate responses, and perform various tasks.

chatbot using natural language processing

Various NLP techniques can be used to build a chatbot, including rule-based, keyword-based, and machine learning-based systems. Each technique has strengths and weaknesses, so selecting the appropriate technique for your chatbot is important. A chatbot is an AI-powered software application capable of conversing with human users chatbot using natural language processing through text or voice interactions. By the end of this guide, beginners will have a solid understanding of NLP and chatbots and will be equipped with the knowledge and skills needed to build their chatbots. This step will create an intents JSON file that lists all the possible outcomes of user interactions with our chatbot.