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.
But—
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:
- What is NLP for chatbots
- How does NLP work
- Common challenges of NLP bot development
- Different methods of developing an NLP bot
- NLP chatbot tutorial
Design your own intelligent chatbots with NLP features and entity recognition
If you are interested in learning more about chatbots, you might also want to read:
- Best Real Life Chatbot Examples From Famous Brands
- Conversation Flowchart & Tree Diagram for Bots
- Easy Ways to Use Chatbots for Business
OK—
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?
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 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 to perform various tasks, such as machine translation, sentiment analysis, speech recognition, and topic segmentation.
Natural language understanding 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 algorithms and processes involved, NLP generally relies 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 can then use these rules to process and generate utterances of a conversation.
What does the training look like?
First, NLP chatbots are 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 chatbot app 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 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.
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 NLP chatbots.
- They’re not perfect
NLP chatbots 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, NLP 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
An NLP chatbot 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
NLP 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. 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. On average, chatbots can solve about 70% of all your customer queries.
In fact, the AI chatbot market is projected to reach over $100 billion by 2026. That’s completely understandable, as many consumers now prefer using AI self-service tools to get their questions answered instead of waiting on hold for customer service. There are countless use cases for chatbots and many businesses start to notice the benefits of using chatbots.
Just keep the above-mentioned aspects in mind, so you can set realistic expectations for your chatbot project.
Now that you know the basics of 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:
- Developing an NLP chatbot from scratch
- Using an existing chatbot framework
- Designing bots with an NLP chatbot platform
Let’s take a look at each of these methods in more detail.
1. NLP chatbot in Python
If you decide to develop your own NLP 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.
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 natural language processing, 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 options available online.
The most popular choices include Microsoft Bot Framework, Amazon Lex, 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 bot frameworks 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.
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 chatbots. From the user’s perspective, they just need to type or say something, and the bot will know how to respond.
Set up your own chatbot with natural language processing functionalities
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.
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 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
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: Best Chat Widgets for Your Website
2. Add channels that your chatbot will be available on
Next, you’ll need to add the channels that you want to automate—Facebook Messenger, Instagram, or web-based chat. You can integrate your chatbot with all of them for multichannel communication or pick just one to start with. Some functionalities and chatbot triggers are only available on certain channels. The NLP feature, however, works the same way on all platforms.
Read more: Best Facebook Chatbots And How to Make Your Own
3. Train your chatbot with popular customer queries
Now it’s time to train your chatbot. 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 NLP in English, French, Spanish, and other languages.
Read more: Popular Customer Database Solutions
4. Design conversation trees and bot behavior
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: Good Practices for Chatbot Design
5. Monitor your results to improve customer experience
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: 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.
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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