Imagine if part of your work was performed by a computer. Like, some intelligent computer may work as information on your website or answer customer calls… Fascinating, isn’t it?
And that’s thanks to the implementation of Natural Language Processing into chatbot software.
Natural language processing can greatly facilitate our everyday life and business. In this blog post, we will tell you how exactly to bring your NLP chatbot to live.
Fields of Natural Language Processing
NLP is a branch of informatics, mathematical linguistics, machine learning, and artificial intelligence. NLP helps your chatbot to analyze the human language and generate the text. Let’s have a look at the core fields of Natural Language Processing.
Natural language understanding
Our language is a highly unstructured phenomenon with flexible rules. If we want the computer algorithms to understand these data, we should convert the human language into a logical form.
Natural language generation
With the help of natural language understanding (NLU) and natural language generation (NLG), it is possible to fully automate such processes as generating financial reports or analyzing statistics.
Natural language interaction
After the previous steps, the machine can interact with people using their language. All we need is to input the data in our language, and the computer’s response will be clear.
Types of Chatbots
Despite the number of programs and natural language processing tools, chatbots and natural language processing technology can be divided into two types:
When encountering a task that has not been written in its code, the bot will not be able to perform it.
Artificially Intelligent Chatbots
These are based on NLP. Natural language processing for chatbot makes such bots very human-like. They react to the meaning of the whole question. The AI-based chatbot can learn from every interaction and expand their knowledge.
An NLP based chatbot is a computer program or artificial intelligence that communicates with a customer via textual or sound methods.
Such programs are often designed to support clients on websites or via phone.
The chatbots are generally used in messaging applications like Slack, Facebook Messenger, or Telegram. They can order your food, buy tickets, or show the weather podcasts.
Challenges For Your Chatbot
It’s the 21st century: a time when computers aren’t just huge calculation machines. Modern computers can understand natural speech and react to it. Thanks to NLP, we can communicate.
But the human language is chaotic despite its structure.
So how does exactly natural language processing work? We have a lot of elements in our speech that affect the understanding of a natural language processing chatbot and may become challenges in natural language processing:
- Synonyms, homonyms, slang
- Omitting punctuation rules
- Different accents
People can understand the meaning of context, intonation, body language, experience. We can understand how a chatbot using natural language processing works: as far as the machine does not have this linguistic experience, NLP implies teaching the computer to understand the speech despite the distractors.
How to Use Chatbot in Business
The bots can provide phone or chat support answering the simple questions. Meanwhile, the complicated tasks pass to a human operator. Here are examples of chatbot use cases:
Customer service chatbot
The NLP for chatbots can provide clients with information about any company’s services, help to navigate the website, order goods or services (Twyla, Botsify, Morph.ai).
Morph.ai chatbot tool
Natural language processing chatbot can help in booking an appointment and specifying the price of the medicine (Babylon Health, Your.Md, Ada Health).
Ada Health Chatbot
The program can inform you about the ticket prices, places of interest, restaurants, souvenir shops, etc. (Skyscanner, Cheapflights Chat, Roomfilia).
A chatbot can assist customers when they are choosing a movie to watch or concert to attend. By answering frequently asked questions, a chatbot can guide a customer, offer a customer the most relevant content.
For example, PVR Cinemas – a film entertainment public ltd company in India – has such a chatbot to assist the customers with choosing a movie to watch, booking tickets, or searching through movie trailers.
PVR Cinemas chatbot
An in-app chatbot can send customers notifications and updates while they search through the applications. Such bots help to solve various customer issues, provide customer support at any time, and generally create a more friendly customer experience.
Duolingo’s chatbot is a great example here. A language-learning service operates an in-app support chatbot (aka Duolingo owl) that provides customers tips during the studying process, reminds about lessons, or informs if there are some service upgrades.
Duolingo In-App Support
With chatbots, you save time by getting curated news and headlines right inside your messenger.
CityFALCON Voice Assistants
2 Ways to Build an NLP Chatbot: Custom Development vs Ready-Made Solutions
Chatbot platforms allow you to make your chatbot by yourself. This is a popular solution for vendors that do not require complex and sophisticated technical solutions.
Pros of Ready-made solutions
- Fast and simple. Ready-made tools are great when you need to build NLP chatbot, but do not have resources to write code.
- Integrations. Most ready-made platforms provide your future chatbot with built-in integrations of such messaging platforms like Messenger, Telegram, Skype, as well as such third-party services as payment getaways.
- Budget prices. It is possible to find chatbot-building platforms that have budget-friendly or even free prices for usage.
Cons of Ready-made solutions
- Poor functionality. Ready-made tools can only provide your future chatbot with a few basic features and simple logic.
- Hard to customize. Once you need to add some features or extend the functionality of your chatbot, this may appear impossible due to ready-made tools functionality restrictions.
Ready-made chatbot tools: pros and cons
TOP Ready-made solutions
- HubSpot’s Chatbot Builder
With HubSpot chatbot builder, it is possible to create a chatbot with NLP to book meetings, provide answers to common customer support questions. Moreover, the builder is integrated with a free CRM tool that helps to deliver personalized messages based on the preferences of each of your customers.
Botsify allows its users to create artificial intelligence-powered chatbots. The service can be integrated both into a client’s website or Facebook messenger without any coding skills. Botsify is integrated with WordPress, RSS Feed, Alexa, Shopify, Slack, Google Sheets, ZenDesk, and others.
Wit.ai is a free chatbot software that lets you easily create text or voice-based bots on your preferred messaging platform. Wit.ai learns human language from every interaction and leverages the community: what’s learned is shared across developers.
BotKit is a leading developer tool for building chatbots, apps, and custom integrations for major messaging platforms. BotKit has an open community on Slack with over 7000 developers from all facets of the bot-building world, including the BotKit team.
TARS enables individuals and businesses to create chatbots. Such bots can be made without any knowledge of programming technologies. The most common bots that can be made with TARS are website chatbots and Facebook Messenger chatbots.
Custom Chatbot Development
If you want to create a sophisticated chatbot with your own API integrations, you can create a solution with custom logic and a set of features that ideally meet your business needs. So how to build a chatbot using NLP? There are some pros and cons, too.
Pros of Custom Development
- Customization. With custom development, you can make your chatbot using NLP complex and unique. There are no restrictions: just conduct a discovery phase to learn more about your target audience and choose the best features for your own chatbot.
- Expertise. You can choose a team that has expertise in particular technologies. For example, you can choose an agency that has expertise in Python or specializes in chatbots developments. The chatbot experts would be able to tailor your chatbot software exactly for your needs.
- Testing & Maintenance. When you choose custom development for your chatbot, you can be sure that the team will not only develop but test and maintain your chatbot in the future. Such an approach helps to ensure that your chatbot will bug-free and will work properly even after further technical upgrades.
Cons of Custom Development
- Time. Once you start developing your custom chatbot, you may need more time for development. In general, chatbot development requires from a few hours to several weeks
- Costs. When you use ready-made tools, you should pay only fees. When you choose custom development, each feature of your chatbot NLP will cost money.
- Development. Well, the process of in-house chatbot development may also be an issue when you do not have time or expertise in that field. Thus it could be much easier and cost-efficient to hire offshore chatbots developers.
Custom chatbot development: pros and cons
How to Build a Chatbot Using NLP: 5 Steps to Take
Business logic analysis
This step is required so the developers’ team can understand our client’s needs. To analyze business logic, a team usually needs to conduct a discovery phase, study the competitive market, determine the core features of your future chatbot and, finally, create the business logic of your future product.
Channel and technology stack
If you would like to create a voice chatbot, it is better to use the Twilio platform as a base channel. On the other hand, when creating text chatbots, Telegram, Viber, or Hangouts are the right channels to work with.
Concerning the tech stack for chatbot development, the most popular and commonly used technologies are:
- Python – a programming language used to build an architecture of your future chatbot
- Pandas – a software library is written for the Python programming language for data manipulation and analysis
- Twilio – allows software developers to programmatically make and receive phone calls, send and receive text messages, and perform other communication functions using its web service APIs
- TensorFlow – a library that is often used for the machine learning and neural networks tasks
- SpaCy – an open-source software library for advanced natural language processing
- Telegram, Viber, or Hangouts APIs – to connect chatbot to your messengers or websites
Development & NLP Integration
The creation of the machine learning chatbot consists of two steps: the development of client-side bot and connecting it to the provider’s API (Telegram, Viber, Twilio, etc.). Once we are done with the development, we can add NLP in chatbots by connecting artificial intelligence.
Once the bot is ready, we start asking the questions that we taught chatbot to answer. As usual, there are not that many scenarios to be checked so we can use manual testing. Testing helps to determine whether your AI NLP chatbot works properly.
Artificial intelligence chatbot can attract more users, save your time, and raise the status of your site. Therefore, the more users are attracted to your website, the more profit you will get.
Our Expertise in Chatbot Development
CallMeBot was designed to help a local British car dealer with car sales. This calling bot was designed to call the customers, ask them questions about the cars they want to sell or buy, and then, based on the conversation results, give an offer on selling or buying a car.
We had to create such a bot that would not only be able to understand human speech like other bots for a website, but also analyze it, and give an appropriate response.
Working on CallMeBot project, we provided our product with extended functionality:
- To check car ads and gather information about mileage, residing city, registration number, service history, and phone number
- To respond to clients’ requests
- To recognize license plate numbers using a picture
- To work with different regions and phone number types
- To detect a client’s region and use it to make phone calls to this client
CityFALCON Voice Assistants
Surely, Natural Language Processing can be used not only in chatbot development. It is also very important for integration of voice assistants and building other types of software.
While we integrated the voice assistants’ support, our main goal was to set up voice search. Therefore, the service customers got an opportunity to voice-search the stories by topic, read, or bookmark. Also, an NLP integration was supposed to be easy to manage and support.
As a result of our work, now it is possible to access CityFALCON news, rates changing, and any other kinds of reminders from various devices just using your voice. Such an approach is really helpful, as far as all the customer needs is to ask, so the digital voice assistant can find the required information.
In this article, we covered fields of Natural Language Processing, types of modern chatbots, usage of chatbots in business, and key steps for developing your NLP chatbot.
Planning your first chatbot, you should go through these stages:
- Study your competitors
- Determine the core features of your future chatbot
- Choose between custom development and ready-made tools
- Find a team of chatbots experts
- Test and maintain your chatbot once the development process is over.
If you have got any questions on NLP chatbots development, we are here to help. You are welcome to book a Free 30-minute consultation.