How to Build A Chatbot with Deep NLP?

Earlier,chatbots used to be a nice gimmick with no real benefit but just another digital machine to experiment with. However, they have evolved into an indispensable tool in the corporate world with every passing year. 

Developing and maintaining a chatbot is, of course, a time, effort and money draining job. Yet, compelling businesses, new and established, to try their luck at this amazingly humane and disruptive technology?

As businesses strive to ensure that customers have access to the relevant information at all times, at any place, and on any given day, the integration of conversational chatbots into corporate platforms or websites appears to be unavoidable.

• According to Markets and Markets estimate, the NLP industry is said to expand from $10.2 billion in 2019 to $26.4 billion in 2024, representing a 21% CAGR.
• The same research also predicted the increase of the conversational AI industry from $4.2 billion in 2019 to $15.7 billion in 2024, with a CAGR of 30.2%, which is higher than the whole NLP market.
• As per IBM, chatbots can help businesses save on customer service costs by improving and speeding up response time, providing agents more time for other challenging work, and answering up to almost 80% of routine questions.
• Some reports by Outgrow state that 80% of businesses are projected to integrate some form of chatbot system by 2021.
• Chatbots have grown in popularity to the point where the number of chatbots on Facebook Messenger has expanded from 100K to 300K in just one year.
  
• Chatbot integration in business platforms or websites is inevitable, as today companies are trying to ensure access to the right information to the customers—anytime, anywhere, any day.
  
• Many popular corporate business brands, such as MasterCard, have also quickly developed their own chatbots. Chatbots are impacting the corporate world in the most surprising and exciting ways, from American Express’s customer service to Google Pixel’s call screening software, granting quick response and 24/7 availability while serving the customers.

But, before we get into how your company might benefit from a deep learning chatbot, let’s have a quick glance at what a deep learning chatbot is? 

Deep learning chatbot is a form of chatbot that uses natural language processing (NLP) to map user input to an intent, with the goal of classifying the message for a prepared response. The trick is to make it look as real as possible by acing chatbot development with NLP.

Based on the sophisticated deep learning and natural language understanding, a chatbot is an intelligent piece of AI-powered software that enables robots to process, comprehend and respond through Natural Language Understanding (NLU).

Modern NLP (natural Language Processing)-enabled chatbots are no longer distinguishable from humans. And thanks to the incorporation of NLP into chatbot software, our daily lives and businesses can be substantially facilitated or made easy as the chatbots are now able to recognize the exact intent of the users, just like humans can interpret each other’s language.

While pursuing chatbot development using NLP, your goal should be to create one that requires little or no human interaction. There are two ways to accomplish this. 

• The first way is suggestions from AI. Here the customer care staff receives suggestions from AI (upon data collection and interpretation) to improve customer service procedures. 
• The second is the NLP technique for chatbot that uses deep learning to handle all of the discussions and eliminates the need for a customer service representative.

Concept of An Intent While Building A Chatbot

The intention of a user interacting with a chatbot, or the intention behind each message received by the chatbot from a specific user, is referred to as “intent.” 

These intents may differ from one chatbot solution to the next, depending on the domain in which you are designing a chatbot solution. 

Hence it is extremely crucial to get the right intentions for your chatbot with relevance to the domain that you have developed it for, which will also decide the cost of chatbot development with deep NLP. 

For example, the voice-enabled chatbot of a travel company will respond to a related set phrases like travel recommendations for a particular city or authentic food options for a particular town or what and where to shop for local handicrafts etc. 

So, why is it necessary to define these intentions? 

Intent is an extremely important aspect to grasp. Your chatbot must be able to understand what the users say or want to do in order to answer queries, search from a domain knowledge base, and conduct numerous other actions in order to continue dialogues with the user. 

As a result, your chatbot must be able to identify the user’s intent from their messages.

How can you make your chatbot understand intents so that it understands what  people want and responds appropriately? 

To become a part of your customer’s choice, it is important for you and your organization to shape the future with bots.The strategy here is to integrate your chatbot development with deep NLP for most accurate intent recognition and production of appropriate responses.

Now it’s time to delve deeper into the inner workings of today’s sophisticated chatbots using NLP. Let’s have a read in the next section regarding how NLP Chatbot is built?

How to Build an NLP Chatbot?

Tokenizing, normalising, identifying entities, dependency parsing, and generation are the five primary stages required for the NLP chatbot to read, interpret, understand, create, and send a response. 

Let’s look more closely at how NLP works in chatbots.

1. Business Logic Analysis 

This stage is necessary so that the development team can comprehend our client’s requirements. A team must conduct a discovery phase, examine the competitive market, define the essential features for your future chatbot, and then construct the business logic of your future product.

2. Channel and Technology Stack 

It is preferable to use the Twilio platform as a basic channel if you want to build NLP chatbot. Telegram, Viber, or Hangouts, on the other hand, are the best channels to use for constructing text chatbots. 

The most prominent and widely used technologies for chatbot development with deep NLP tools are:

• Python – A programming language for creating the NLP architecture of your chatbot.
• Pandas – For data processing and analysis for the chatbot, a software library is built using Pandas for the Python programming language. 
• Twilio – Software developers can use its web service APIs to programmatically make and receive phone calls, send and receive text messages, and perform other communication tasks for your chatbot.
• TensorFlow – A library that is frequently used for tasks involving machine learning and neural networks to help your chatbot interpret intentions better.
• SpaCy – A sophisticated natural language processing open-source software library, SpaCy clarifies the user intent with a more comprehensive language library.
• Telegram, Viber, or Hangouts APIs – To integrate a NLP based chatbot with your messaging apps or websites.

3. Development & NLP Integration

The building of a client-side bot and connecting it to the provider’s API are the first two phases in creating a machine learning chatbot. 

Once the work is complete, you may integrate AI with NLP which helps the chatbot in expanding its knowledge through each and every interaction with a human. For this, you can approach an AI chatbot development company. 

• Tokenizing: The chatbot development begins by breaking up text into small chunks (known as “tokens”) and deleting punctuation.
• Normalizing: The bot then searches the text for common misspellings, slang, or typos and transforms them to the “normal” version.
• Recognizing Entities: After all of the words have been normalised, the chatbot attempts to determine what is being said. It would recognise North America as a region, 67% as a proportion, and Google as a firm, for example.
• Dependency Parsing: The bot then divides the sentence into nouns, verbs, objects, punctuation, and common phrases in the next step.
• Generation: Finally, the chatbot develops a number of responses based on the data gathered in the previous phases and chooses the most appropriate one to send to the user.

4. Testing

In the testing phase, we start asking the questions that we taught the chatbot using NLP to answer once it’s ready. We can utilise manual testing to make sure that the chatbot gathers more data and provides appropriate response. 

Testing can assist you figure out if your AI NLP tools for chatbot development process is at par. 

A chatbot powered by artificial intelligence can help you attract more users, save time, and improve the status of your website. As a result, the more people that visit your website, the more money you’ll make.

Businesses all over the world are turning to bots to reduce customer service costs and deliver round-the-clock customer service. Chatbots are powered by very conventional technology. NLP has a long way to go, but it already holds a lot of promise for chatbots in their current condition.

Concluding Thoughts

The field of chatbots continues to be tough in terms of how to improve answers and selecting the best model that generates the most relevant answer based on the question, among other things.

One of the most striking aspects of intelligent chatbots is that with each encounter, they become smarter. Machine learning chatbots, on the other hand, are still in primary school and should be closely controlled at the beginning. NLP is prone to prejudice and inaccuracy, and it can learn to talk in an objectionable way.

You’re ready to develop and release your new chatbot mastermind into the world now that you know how NLP, machine learning, and chatbots function. It’s finally time to allow the chatbot development service of a trustworthy chatbot app development company to help you serve as a friendly and knowledgeable representative at the front of your customer service team. 

If you’re interested in building chatbots, then you’ll find that there are a variety of powerful chatbot development platforms, frameworks, and tools available. 

Thus, rather than adopting a bot development framework or another platform, why not hire a chatbot development company to help you build a basic, intelligent chatbot using deep learning. 

It’s time to automate and streamline your customer service with the most agile platform for developing NLP for chatbot through the best, most compatible and high-end chatbot app development company in USA, as well as other regions.

THE AUTHOR

Prateek Saxena

DIRECTOR & CO-FOUNDER