What is NLP & why does your business need an NLP based chatbot?

In this post we will face one of these tasks, specifically the “QA with single supporting fact”. Because of this today’s post will cover how to use Keras, a very popular library for neural networks to build a simple Chatbot. The main concepts of this library will be explained, and then we will go through a step-by-step guide on how to use it to create a yes/no answering bot in Python. We will use the easy going nature of Keras to implement a RNN structure from the paper “End to End Memory Networks” by Sukhbaatar et al (which you can find here). Based on these pre-generated patterns the chatbot can easily pick the pattern which best matches the customer query and provide an answer for it.

You’ll experience an increased customer retention rate after using chatbots. It reduces the effort and cost of acquiring a new customer each time by increasing loyalty of the existing ones. Chatbots give the customers the time and attention they want to make them feel important and happy. NLP analyses complete sentence through the understanding of the meaning of the words, positioning, conjugation, plurality, and many other factors that human speech can have.

Traditional Chatbots Vs NLP Chatbots

The use of NLP is growing in creating bots that deal in human language and are required to produce meaningful and context-driven conversions. NLP-based applications can converse like humans and handle complex tasks with great accuracy. The use of Dialogflow and a no-code chatbot building platform like Landbot allows you to combine the smart and natural aspects of NLP with the practical and functional aspects of choice-based bots. Generally, the “understanding” of the natural language (NLU) happens through the analysis of the text or speech input using a hierarchy of classification models.

There is no single API that does intent and entity recognition in a single call. Wit.ai allows controlling the conversation flow using branches and also conditions on actions (e.g. show this message only if some specific variables are defined). To interact with the server side, you have “Bot sends” commands, which basically calls to functions. A very interesting point is that you can set the role of the entities in a phrase. For example, in “I want to fly to Venice, Italy from Paris, France, on January 31”, you can state that the first city is the destination and the second one the departure.

Key elements of NLP-powered bots

It’s vital because it ensures you understand and get value from what you bought, keeps you happy and staying on, and cuts down on people leaving by making an excellent first impression. After this, we need to calculate the output o adding the match matrix with the second input vector sequence, and then calculate the response using this output and the encoded question. In 2015, Facebook came up with a bAbI data-set and 20 tasks for testing text understanding and reasoning in the bAbI project. On the left part of the previous image we can see a representation of a single layer of this model. Okay, now that we know what an attention model is, lets take a loser look at the structure of the model we will be using. This model takes an input xi (a sentence), a query q about such sentence, and outputs a yes/ no answer a.

Now when you have identified intent labels and entities, the next important step is to generate responses. In the response generation stage, you can use a combination of static and dynamic response mechanisms where common queries should get pre-build answers while complex interactions get dynamic responses. The chatbot will keep track of the user’s conversations to understand the references and respond relevantly to the context.

Natural Language Processing is a 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 contextual analysis similar to a human being. The move from rule-based to NLP-enabled chatbots represents a considerable advancement. While rule-based chatbots operate on a fixed set of rules and responses, NLP chatbots bring a new level of sophistication by comprehending, learning, and adapting to human language and behavior. Natural language processing is a specialized subset of artificial intelligence that zeroes in on understanding, interpreting, and generating human language.

If there is no intent matching a user request, LUIS will find the most relevant one which may not be correct. Unfortunately, there is no option to add a default answer, but there is a predefined intent called None which you should teach to recognize user statements that are irrelevant to your bot. When a chatbot is successfully able to break down these two parts in a query, the process of answering it begins. NLP engines are individually programmed for each intent and entity set that a business would need their chatbot to answer.

Online stores deploy NLP chatbots to help shoppers in many different ways. A user can ask queries related to a product or other issues in a store and get quick replies. Here are three key terms that will help you understand how NLP chatbots work. BUT, when it comes to streamlining the entire process of bot creation, it’s hard to argue against it. While the builder is usually used to create a choose-your-adventure type of conversational flows, it does allow for Dialogflow integration. Another thing you can do to simplify your NLP chatbot building process is using a visual no-code bot builder – like Landbot – as your base in which you integrate the NLP element.

We’ll tokenize the text, convert it to lowercase, and remove any unnecessary characters or stopwords. As a cue, we give the chatbot the ability to recognize its name and use that as a marker nlp for chatbots to capture the following speech and respond to it accordingly. This is done to make sure that the chatbot doesn’t respond to everything that the humans are saying within its ‘hearing’ range.