The Role of Natural Language Processing NLP in Chatbot Development

Humans take years to conquer these challenges when learning a new language from scratch. Programmers have integrated various functions into NLP technology to tackle these hurdles and create practical tools for understanding human speech, processing it, and generating suitable responses. In terms of the learning algorithms and processes involved, language-learning chatbots generally rely heavily on machine-learning methods, especially statistical methods. They allow computers to analyze the rules governing the structure and meaning of language from data. Apps such as voice assistants and NLP-based chatbots can then use these language rules to process and generate utterances of a conversation. Deploying a rule-based chatbot can only help in handling a portion of the user traffic and answering FAQs.

Today almost all industries use chatbots for providing a good customer service experience. In one of the reports published by Gartner, “ By 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis”. In this article, we will learn about different types of chatbots using Python, their advantages and disadvantages, and build a simple rule-based chatbot in Python (using NLTK) and Python Tkinter.

Theoretically, humans are programmed to understand and often even predict other people’s behavior using that complex set of information. Frankly, a chatbot doesn’t necessarily need to fool you into thinking it’s human to be successful in completing its raison d’être. At this stage of tech development, trying to do that would be a huge mistake rather than help. Still, all of these challenges are worthwhile once you see your NLP chatbot in action, delivering results for your business.

Key differences between NLP chatbot and rule-based chatbot

It follows a set rule and if there’s any deviation from that, it will repeat the same text again and again. However, customers want a more interactive chatbot to engage with a business. Training AI with the help of entity and intent while implementing the NLP in the chatbots is highly helpful. By understanding the nature of the statement in the user response, the platform differentiates the statements and adjusts the conversation. With its intelligence, the key feature of the NLP chatbot is that one can ask questions in different ways rather than just using the keywords offered by the chatbot.

When a user enters a message to the chatbot, it must use algorithms to extract significance and context from each sentence in order to gather data. Improvements in NLP models can also allow teams to quickly deploy new chatbot capabilities, test out those abilities and then iteratively improve in response to feedback. Unlike traditional machine learning models which required a large corpus of data to make a decent start bot, NLP is used to train models incrementally with smaller data sets, Rajagopalan said. To achieve this, the chatbot must have seen many ways of phrasing the same query in its training data. Then it can recognize what the customer wants, however they choose to express it.

What Can NLP Chatbots Learn From Rule-Based Bots

In the below image, I have shown the sample from each list we have created. Artificial intelligence is all set to bring desired changes in the business-consumer relationship scene. In addition, the existence of multiple channels has enabled countless touchpoints where users can reach and interact with.

If you want to create a sophisticated chatbot with your own API integrations, you can create a solution with custom logic and a set of features that ideally meet your business needs. Once the bot is ready, we start asking the questions that we taught the chatbot to answer. As usual, there are not that many scenarios to be checked so we can use manual testing. Testing helps to determine whether your AI NLP chatbot works properly.

Kompose offers ready code packages that you can employ to create chatbots in a simple, step methodology. If you know how to use programming, you can create a chatbot from scratch. If not, you can use templates to start as a base and build from there. NLP is capable of differentiating different types of customer requests. A personalized approach in responding to these requests significantly enhances customer experience.

• Then, give the bots a dataset for each intent to train the software and add them to your website.
• For example, the Facebook model has been trained on 2,200 languages and can directly translate any pair of 100 languages without using English data.
• If your company tends to receive questions around a limited number of topics, that are usually asked in just a few ways, then a simple rule-based chatbot might work for you.
• This, coupled with a lower cost per transaction, has significantly lowered the entry barrier.

Contrary to the common notion that chatbots can only use for conversations with consumers, these little smart AI applications actually have many other uses within an organization. Here are some of the most prominent areas of a business that chatbots can transform. One of the major reasons a brand should empower their chatbots with NLP is that it enhances the consumer experience by delivering a natural speech and humanizing the interaction.

These platforms have some of the easiest and best NLP engines for bots. From the user’s perspective, they just need to type or say something, and the NLP support chatbot will know how to respond. Chatbots that use NLP technology can understand your visitors better and answer questions in a matter of seconds. On average, chatbots can solve about 70% of all your customer queries. This helps you keep your audience engaged and happy, which can increase your sales in the long run. NLP enables the computer to acquire meaning from inputs given by users.

To extract the city name, you get all the named entities in the user’s statement and check which of them is a geopolitical entity (country, state, city). To do this, you loop through all the entities spaCy has extracted from the statement in the ents property, then check whether the entity label (or class) is “GPE” representing Geo-Political Entity. If it is, then you save the name of the entity (its text) in a variable called city.

NLP chatbots are pretty beneficial for the hospitality and travel industry. With ever-changing schedules and bookings, knowing the context is important. Chatbots are the go-to solution when users want more information about their schedule, flight status, and booking confirmation. It also offers faster customer service which is crucial for this industry.

As NLP continues to evolve, developers are experimenting with advanced technologies to enhance their amazing capabilities. With enhanced language models, sophisticated algorithms, and better semantic interpretation, chatbots will continue to replicate human responses. No wonder, eCommerce brands and businesses operating digitally can exploit the advantages of smart chatbot development. Well, in the backdrop of the evolution of powerful chatbots, the NLP technology stands tall.

It also takes into consideration the hierarchical structure of the natural language – words create phrases; phrases form sentences;  sentences turn into coherent ideas. Natural Language Processing does have an important role in the matrix of bot development and business operations alike. The key to successful application of NLP is understanding how and when to use it.

Why NLP chatbot?

But for many companies, this technology is not powerful enough to keep up with the volume and variety of customer queries. NLP chatbots are still a relatively new technology, which means there’s a lot of potential for growth and development. Here are a few things to keep in mind as you get started with natural language bots. As you can see, setting up your own NLP chatbots is relatively easy if you allow a chatbot service to do all the heavy lifting for you.

As programmed, they match these words with available entities and collect the programmed ones to complete a task. As chatbots become more prevalent in various industries, ethical considerations will play a significant role in their development. Chatbots will be designed with robust privacy and security measures, with a focus on data protection and user consent. Ethical guidelines will be established to govern the use of chatbots, ensuring fair and unbiased interactions. Once the training data is prepared in vector representation, it can be used to train the model.

Depending on the industry, the nature of this intent significantly varies. For instance, a customer looking for the best pizza corners in a food delivery app would have a different intent than someone shopping for medicines. The graph reveals that the global chatbot market is set to reach the milestone of $1.25 billion in 2025. Preprocessing plays an important role in enabling machines to understand words that are important to a text and removing those that are not necessary. It’s fast, ideal for looking through large chunks of data (whether simple text or technical text), and reduces translation cost. 3) The chatbot sends the gathered data (intents and entities) to the decision-making engine.

Using the same concept, we have a total of 128 unique root words present in our training dataset. One of the customers’ biggest concerns is getting transferred from one agent to another to resolve the query. Chatbots primarily employ the concept of Natural Language Processing in two stages to get to the core of a user’s query. Businesses deploying smart bots have customers who reach out to their helpdesk with specific intents.

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