Understanding How NLP works on a Chatbot

Chatbots have become more prevalent over the past years. And with the advent of it, accomplishing tasks at scale has become easy. However, there is always a crowd who carries a sentiment every time they have a conversation with a chatbot- “It is not understanding what I’m speaking.”

This is where NLP (Natural Language Processing) comes into the picture. NLP (Natural Language Processing) is the science of deducing the intention (Intent) and related information (Entity) from natural conversations. Within the right context for the right use case, NLP paves the way for an easy to use interface. But more importantly, an NLP based chatbot gives the users on the other side of the screen that they are having a conversation with a live person as opposed to going through a maze of menus and options to reach their goal.

Why need a Natural Language Processing?

Say you have a chatbot for customer service, it is likely that customers will ask questions to the bot that will sometimes go beyond the bot’s reach and throw it off. But this situation can be resolved by having default responses in place, however as it is not possible to guess the kind of questions a customer may ask or how they will be asked. But with Natural Language Processing, developers can train the chatbot on multiple conversations it will go through as well as providing multiple examples of content it will be in contact with so that it can interpret queries more effectively.

The need for NLP is dependent on how your chatbot is built is what is that you want to accomplish by it.

First, if your chatbot is already built, and you have in- ready response data to work with, take a look and see if users ask more questions, as well as how accurately it responds. If a few questions are being asked, NLP likely is not as important for you (though it still has its benefits). If your chatbot is facing a significant amount of questions and responding poorly, NLP can serve as an outstanding method for delivering superior answers more consistently.

Second, if you’re starting to build a chatbot from scratch, consider its intent. Will it have a personality? And if you’d like your chatbot to be highly conversational, and see it as having a question-and-response style, NLP is essentially a must-have.

NLP-powered Chatbots can help any business achieve success. But before that, the organization needs to plan a marketing strategy. A marketing strategy is a comprehensive plan capsulated to achieve the marketing objective. Just like other business endeavors, it is important to make sure you have a good comprehensive plan that is precise and well researched. Through ensuring you have a proper marketing strategy, you are guaranteed you will ultimately achieve success. The same is important when it comes to using chatbots for marketing.

To get started, you have to determine the problem that you are addressing. Let’s assume that the goal that you want to improve marketing. For example:

  • Building the reputation
  • Increase engagement on your website
  • Drive brand awareness

Also read, The different ways to add music to an Instagram video

How to implement Natural Language Processing?

Implementing NLP is challenging, and it is largely driven by the platform you choose to use. In most of the experiences, an enterprise-chatbot platform like Kore.ai is the best bet. Most platforms only use machine learning (ML) for natural language processing. An ML-only approach requires extensive training of the bot for high success rates. Inadequate training can cause inaccurate results. As training data, one must provide a collection of sentences (utterances) that match a chatbot’s intended goal and eventually a group of sentences that do not. When the bot uses ML, it does not understand an input sentence. Instead, it measures the similarity of data input to the training data imparted to it.

Kore.ai combines Fundamental Meaning (FM), Machine Learning (ML) and Knowledge Graph (KG) making it easy to build natural language capable chatbots, irrespective of the extensiveness of training provided to the bot. Enterprise developers can solve real-world dynamics by leveraging the inherent benefits of these approaches and eliminating their shortcomings.

Overall, NLP is likely the next step in bridging some of the fallbacks that users, businesses, and developers experience with chatbots. It fills gaps wherever they fail and help ensure that your chatbot is one that anyone can enjoy interacting with.

Architecture Diagram of How Natural Language Processing Works?

  1. You find a product on Facebook’s Messenger and let’s say it’s a bouquet. You send the bot a message that is picked up by the backend saying you want a bouquet. Using Natural Language Processing (what happens when computers read the language. NLP processes turn text into structured data), the machine converts this plain text request into codified commands for itself.
  2. Now the chatbot throws this data into a decision engine since in the bot’s mind it has certain criteria to meet to exit the conversational loop, notably, the type of flowers you want.
  3. Using Natural Language Generation (what happens when computers write the language. NLG processes turn structured data into text), much like you did with your mother the bot asks you where do you want it to be delivered? This array of responses goes back into the messaging backend and is presented to you in the form of a question. You tell the bot you want 1 bouquet of roses and we go back through NLP into the decision engine.
  4. The bot now analyzes pre-fed data about the product, stores, their locations and their proximity to your location. It identifies the closest store that has this product in stock and tells you what it costs.
  5. It then directs you to a payment portal and after it receives confirmation from the gateway, it places your order for you, and voila in the same or two business days, you have a bouquet of roses delivered at your desired location.

Natural Language Processing plays a crucial role in supporting machine-human interactions.

In the coming years, it is expected to see more breakthroughs that will make machines smarter at recognizing and understanding the human language.

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