What Is a NLP Chatbot & How to Build?

The term chatbot is not limited to any one particular type of chatbot. Instead, a huge variety of chatbots are available on the internet to fulfill different functions and user requirements. Natural language processing (NLP) chatbots are one of such types that you are likely to come across on different platforms.

In this article, we will dive into the various aspects of NLP chatbots, including how they differ from rule-based chatbots and how you can build an NLP chatbot without coding!

So, let’s start with some basics.

Part 1: What NLP Chatbot Is

Natural language is the simple and plain language we humans use in our everyday lives for communication. It is different from a programming language that is used to instruct computers to perform some function.

The purpose of natural language processing (NLP) is to ensure smooth communication between humans and machines without having to learn technical programming languages.

Definition of NLP Chatbot

An NLP chatbot is an accurate and efficient way of describing an AI chatbot. It is a chatbot powered by powerful AI, machine learning, and NLP algorithms to ensure the chatbot can understand the user’s commands in human language and provide relevant results.

How Does an NLP Chatbot Work?

The working of an NLP chatbot involves transforming the given text into structured data that the computers can understand and analyze to give the right output. This is why an efficient NLP chatbot can process large volumes of linguistic data to provide correct interpretations.

Following are some of the other key components involved in the working of an NLP chatbot:

• Analyse and recognize user intent by classifying the input and making deductions about the user’s requirements.
• Thoroughly scanning the text and identifying fundamental entities into categories, such as people, companies, and places.
• Continuous training to ensure vocabulary expansion and increase the accuracy of the NLP chatbot.
• A reliable noun recognition system to ensure the NLP chatbot can differentiate between common and proper nouns.

Part 2: NLP Chatbot vs. Rule-Based Chatbot

When it comes to the different types of chatbots, rule-based chatbots, and NLP chatbots are two of the most popular types of chatbots you are likely to find on the internet. However, there are some key differences between them.

Rule-based chatbots are commonly used by small and medium-sized companies. As the term suggests, rule-based chatbots operate according to pre-defined rules and working procedures. The user's inputs must be under the set rules to ensure the chatbot can provide the right response.

Most the rule-based chatbots have buttons to ensure the users can get answers to their queries by setting prompts easily. Unlike the NLP chatbots, rule-based chatbots do not have advanced machine learning algorithms or NLP training, so they have very limited open conversation options.

NLP chatbots are powered by efficient AI algorithms to understand the different inputs and think and respond like humans. NLP chatbots use extensive amounts of data for training and often have multi-linguistic capabilities to provide reliable customer support.

In short， NLP chatbots understand, analyze, and learn languages just like children. Once they are properly trained, they can make connections between the questions and answers to provide accurate responses.

So rule-based chatbots are limited to a specific set of rules and prompts, but NLP chatbots are much more extensive as they can handle even complex queries in unique and natural language.

Benefits of NLP Chatbots

There are many benefits of NLP chatbots for both personal and professional reasons that make them significantly better than traditional rule-based chatbots:

• NLP chatbots are capable of imitating human-like interactions, so brands can use them to increase engagement.
• They can handle simple as well as complex queries, so implementing NLP chatbots is a great way of automating customer support and minimizing human intervention.
• Since well-trained NLP chatbots retain context throughout the conversations, they can be used to offer personalized support.
• NLP chatbots improve the overall customer support responses and streamline the working procedures of a company.

They speed up the query resolution time and hence help companies reduce their operational cost and allow human agents to work on other complex tasks.

Common Challenges of NLP

While NLP chatbots certainly have numerous benefits and applications, they have some significant challenges as well:

• There are thousands of natural or human languages spoken all over the world. Dealing with different language nuances is a huge challenge while training a multi-linguistic NLP chatbot.
• The quality and efficiency of an NLP chatbot are all dependent on the quantity as well as the quality of the training data. Obtaining a large and relevant dataset is not always easy.
• Creating, designing, implementing, and continuously training NLP chatbots require a significant amount of time and resources, especially if you are doing it through traditional development methods.
• AI algorithms and handling NLP itself can be quite challenging as you have to deal with numerous factors like contextual understanding, semantic analysis, noun recognition, etc.
• Biases in chatbot NLP structure are quite common because there is no such thing as a perfect dataset. Eliminating or mitigating these biases requires significant training.
• Simple grammar mistakes, spelling errors, or words with multiple meanings can cause havoc in the working of an NLP chatbot.

Part 3：NLP Chatbot Use Cases & Examples

Now that you are familiar with all the key aspects of NLP chatbots, let’s closely look at some of the common use cases and specific examples:

1. Lemonade

Insurance is a highly complicated sector. A person looking for insurance often has a lot of questions. As a result, a traditional rule-based chatbot is not enough to fulfill the requirements of such customers. Therefore, Lemonade, a leading insurance company, has created its NLP chatbot called Maya which can understand the user's queries and guide them throughout the process of buying insurance.

2. Mental Health Chatbot

Mental health is a serious topic that has gained a lot of attention in the last few years. Simple hotlines or appointment-scheduling chatbots are not enough to help patients who might require emergency assistance.

As a result, some psychiatrists and mental healthcare service providers are using NLP chatbots to provide immediate support to the users. In this way, a well-designed NLP chatbot can diffuse the situation and encourage the user to visit a medical expert immediately.

3. Mastercard Chatbot

Source:https://www.chatbotguide.org/

Mastercard has an NLP chatbot called KAi to help users get personalized information about their money planning and overall financial management. The purpose of this NLP chatbot is to ensure that users can interact with the chatbot and get expert advice as per their specific circumstances.

KAi is a powerful chatbot to obtain information about financial goals and also other Mastercard services related to card activation and balance questions. Such kinds of NLP chatbots are also implemented by many other banks, such as Bank of America’s Erica, and financial institutes.

Part 4：NLP Tutorial: How to Build NLP Bots Without Coding

With a powerful no-code bot creation platform like GPTBots, you can start building your own NLP bots without any technical knowledge or coding skills.

GPTBots is a powerful platform that has a large collection of bot templates to help you get started. Moreover, it is suitable for both beginners as well as experienced individuals to create bots as it has a user-friendly interface and working process.

Following are the 4 key steps of the NLP tutorial you should follow to create an efficient chatbot:

Create an account on the official website of GPTBots or simply sign in to access its dashboard. Create a new project.

It is recommended that you start with a bot template to ensure you have the necessary settings and configurations in advance to save time.

Now train your NLP chatbot with relevant documents, files, online text, website links, or spreadsheets. Do it by opening the Documents section and uploading your files.

Let's add an article to the knowledge base of your NLP chatbot.

You can also modify the Flow of your bot to ensure it accesses the right knowledge base to provide relevant outputs.

After completing the bot creation and training process, the final step is to integrate your NLP chatbot into a platform or social media channel, such as Slack, WhatsApp, Zapier, etc.

View Chatbot History under the history section after the integration.

Part 5: Future of NLP Chatbots

The NLP chatbots and AI, in general, a continually evolving areas. The market of NLP chatbots is expected to keep growing exponentially in the future. Customers are already getting used to advanced, reliable, and efficient NLP chatbots used by large as well as small businesses.

NLP chatbots are expected to become the first point of contact with customers. So whether a company is selling a product or offering services, it will have to use an NLP chatbot to provide quick information to the customers.

Overall, the applications and possibilities associated with the NLP chatbots are endless. The good thing is that all types and sizes of companies can benefit from such chatbots by easily creating them using the free trial version of GPTBots.

FAQs

• 1

  What is the difference between NLU and NLP chatbots?

  NLP chatbots are focused on analyzing the surface-level of user inputs, such as sentence structure, syntax, and words. On the other hand, NLU focused on extracting deeper meanings often hidden in linguistic expressions.
• 2

  Which algorithm is used in NLP in the chatbot?

  A wide range of algorithms are used in NLP chatbots. These algorithms are a combination of artificial intelligence, machine learning, NLU, and deep learning.
• 3

  Is ChatGPT NLP?

  Yes, ChatGPT is an example of an NLP chatbot as it is powered by powerful large language models to handle complex user queries.