Understanding the Need for NLP in Your Chatbot

As chatbots have become more and more prevalent over the past few years, they have predominantly functioned as scripted, linear conversations where the chatbot’s output is predetermined. For most cases, this works just fine.

However, chatbots are capable of much more than this. With the introduction of Natural Language Processing (NLP), developers are now able to truly put the “chat” in chatbot.

NLP — What is it?

NLP is a technological process that allows computers to derive meaning from user text inputs. In doing so, it attempts to understand the intent of the input, rather than just the information about the intent itself. There are a number of different ways in which this function can be built. These vary, and can be chosen based on how you intend to implement and utilize NLP.

In the context of chatbots, integrating NLP means adding a more human touch. If you’ve built a chatbot and deployed it for public use, it’s likely that you’ve seen users attempting to ask it questions. It seems very much in line with human nature that users will try to stump the chatbot and throw it off. You can attempt to remedy this by adding default responses, however this tends to fall short quite often as it is nearly impossible to predict which questions will be asked, as well as the manner in which they will be asked.

With NLP, you are able to “train” your chatbot on the various interactions it will go through, and help streamline the responses it outputs. For the most part, training consists of providing examples of content it will encounter. Providing more examples to your chatbot gives it a wider basis with which it can interpret and answer questions and commands. This can be somewhat time consuming, but produces results that make it worth it.

NLP — Do I need it?

The necessity of NLP is highly dependant on how your chatbot is built, and what you want it to accomplish.There are a few ways in which this can be determined.

First, if your chatbot is already built, and you have response data to work with, take a look and see if users tend to ask it questions, as well as how accurately it responds. If relatively 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? WIll it be mainly driven by buttons and suggested responses, or instead by raw user inputs? 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. That being said, even the most button-driven chatbot would benefit from having some NLP built in for when users inevitably attempt to ask it questions.

NLP — How do I implement it?

While NLP can seem intimidating, the difficulty of implementing it is largely driven by the platform you choose to use. In my experience, a chatbot programming platform like SnatchBot is your best bet. SnatchBot provides a number of pre-made NLP functions that allow you to get started without any sort coding. Or, if you’re looking for more of a custom solution, the platform is also outstanding at building a set of responses from scratch.

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

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