With the advent and rise of chatbots, we are starting to see them utilize artificial intelligence — especially machine learning — to accomplish tasks, at scale, that cannot be matched by a team of interns or veterans. Even better, enterprises are now able to derive insights by analyzing conversations with cold math. However, there is still a substantial amount of people who carry a common sentiment every time they have a conversation with a bot – “It doesn’t understand what I’m saying.”
This is where Natural Language Processing comes into the picture. Within the right context for the right applications, NLP can pave the way for an easier-to-use interface to features and services. But more importantly, an NLP based chatbot can give the end users on the other side of the screen that they’re having a conversation, as opposed to going through a limited set of options and menus to reach their end goal.
What is Natural Language Processing (NLP)?
Natural Language Processing is based on deep learning that enables computers to acquire meaning from inputs given by users. In the context of bots, it assesses the intent of the input from the users and then creates responses based on a contextual analysis similar to a human being.
Say you have a chatbot for customer support, it is very likely that users will try to ask questions that go beyond the bot’s scope and throw it off. This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised.
When it comes to Natural Language Processing, developers can train the bot on multiple interactions and conversations it will go through as well as providing multiple examples of content it will come in contact with as that tends to give it a much wider basis with which it can further assess and interpret queries more effectively.
So, while training the bot sounds like a very tedious process, the results are very much worth it. Royal Bank of Scotland uses NLP in their chatbots to enhance customer experience through text analysis to interpret the trends from the customer feedback in multiple forms like surveys, call center discussions, complaints or emails. It helps them identify the root cause of the customer’s dissatisfaction and help them improve their services according to that.
What is the Best Approach towards NLP?
The best approach towards NLP is a blend of Machine Learning and Fundamental Meaning for maximizing the outcomes. Machine Learning only is at the core of many NLP platforms, however, the amalgamation of fundamental meaning and Machine Learning helps to make efficient NLP based chatbots. Machine Language is used to train the bots which leads it to continuous learning for natural language processing (NLP) and natural language generation (NLG). Both ML and FM has its own benefits and shortcomings as well. Best features of both approaches are ideal for resolving real-world business problems.
Here’s what an NLP based bot entails –