The Ultimate Guide to NLP Chatbots
Artificial intelligence (AI) has made dramatic improvements in the past few years, particularly in natural language processing. From the first chatbot — a pseudo-therapist named Eliza — to ChatGPT, AI chatbots have evolved from crude, rules-based programs that could only respond with limited phrases to sophisticated programs capable of learning from their interactions. Business use cases are expanding as well, with 84% of business leaders believing chatbots will become more important in business operations over the next five years.
What Are NLP Chatbots?
Natural language processing (NLP) is a branch of AI that allows computers to understand, generate, and manipulate human language similar to how humans do. Since computers were first introduced, the primary method people have used to communicate with them is by using their language: binary code. While binary code is simple — it only contains two elements, usually 1s and 0s — it’s a clunky form of communication. For example, “hello” in binary code is 01001000 01000101 01001100 01001100 01001111.
To bridge the communication gap between computers and humans, developers invented programming languages. However, learning to program requires a lot of time and skill to learn.
Natural language processing allows people to communicate with computers using human language, making it much easier to get information from a computer. NLP combines several types of artificial intelligence, including machine learning and deep learning, with statistical modeling and rules-based modeling of human language, which is called computational linguistics.
There are two primary types of chatbots, rules-based and AI chatbots. Rules-based chatbots, like Eliza and many current chatbots used for tasks such as booking a flight, rely on a series of pre-defined rules based on the types of questions they’ll likely encounter. These rules can be simple or complicated, but the chatbot can’t answer questions outside these rules. A flight-booking chatbot won’t be able to tell you what you should wear to dinner in Milan, for example.
In contrast, AI conversation bots train themselves on huge datasets and can learn from their interactions. They understand the content and intent of a question and generate answers through natural language processing.
How NLP Powers Chatbots
Natural language processing allows chatbots to extract meaning from a user’s question by analyzing sentence structure and pairing those to particular intents or actions. This first step is how the chatbot is able to understand what a person is asking it. It can also draw on previous interactions to refine user intent, which is how it can determine whether a user is referring to the side of a river or a financial institution when they use the word “bank.” This type of conversation with AI is similar to how humans talk with each other and provides better conversation flow.
By analyzing human speech patterns and contextual clues in many datasets, chatbot technology can generate appropriate responses that answer the user’s intent. This allows them to mimic human conversation and engage in natural and meaningful conversations. If the chatbot is integrated with a system API, it can go a step further and perform tasks based on user input rather than simply generating a reply.
Benefits of Implementing NLP in Chatbots
NLP chatbot platforms have several significant advantages over rules-based chatbots, particularly for more open-ended interactions. NLP models can handle much more complexity and continuously improve over time.
Enhanced User Interaction
NLP allows chatbots to engage in more natural, human-like conversations to improve the user experience. They’re capable of extracting meaning from various forms including nuances. People can simply talk — or type — the way they would with another person. They don’t need to learn a specific format or choose from a drop-down menu of limited possibilities.
This natural format makes it easier for people to interact with the chatbot and get what they need. These more intuitive interactions are important in customer service, where customers may already be frustrated and overwhelmed. For example, if a patient in a hospital room says, “It’s freezing in here,” an NLP chatbot can determine that the patient wants to turn up the heat even though the words “temperature” or “cold” were never used.
Effective error handling lets NLP chatbots detect when they don’t understand something and ask for clarification from the user. This reduces user frustration, especially compared to rules-based chatbots that can only respond with irrelevant answers or error codes.
Personalization and Contextual Understanding
NLP allows chatbots to adapt their responses based on the user’s previous interactions and preferences. For example, an NLP text bot can remember a user’s prior conversations and tailor its answers accordingly. It can also put current questions in context based on earlier interactions to provide personalized, relevant responses.
A conversational AI chatbot can also match the user’s tone, which makes its answers more accessible. If a user asks a question in a casual tone, the chatbot can respond in kind.
Increased Efficiency and Automation
AI-powered chatbots can handle a high volume of inquiries simultaneously, reducing the need for expensive human labor to handle routine tasks. According to Gartner, organizations that use chatbots reduce their email, call, or chat inquiries by 70%.
Many routine customer service tasks, like answering FAQ or guiding users through standard processes, can be fully automated with NLP AI chatbot programming. This frees up employees to focus on high-value tasks that can’t be automated.
An intelligent chatbot can also be integrated with other systems, such as customer-relationship management (CRM) databases and inventory management, to provide real-time information and perform complex tasks. For example, in the healthcare industry, a chatbot could check the status of a diagnostic test and inform the healthcare provider accordingly.
AI chatbots that include machine learning capabilities can continuously learn and improve over time. An upfront investment will result in a system that provides ever-increasing returns by providing more effective responses, fewer errors, and an increasingly sophisticated understanding of user questions.
Real-World Applications of NLP Chatbots
NLP for chatbots is already widely used in business, with adoption only expected to grow in the coming years. Some of the most popular applications include the following.
Customer Support Chatbots
Chatbot builders can design chatbots to provide 24/7 customer support so users can get help after hours and on holidays without the company having to pay for expensive overtime. With an increasingly global economy, many customers are likely to be located in different time zones than the companies they do business with.
Businesses can significantly decrease wait times for their customers with chatbots since they can handle a much higher volume of customer service interactions than human agents. Instead of having to wait for a customer service representative, people can have their issues resolved quickly.
NLP chatbots can also provide customized recommendations for customers based on their preferences and purchase history. They can easily access customer data and respond based on individualized parameters, such as the customer’s preferred language.
NLP chatbots make great personal assistants. Siri, Alexa, and Google Assistant all rely on NLP technology. They can perform personalized tasks such as setting reminders, making phone calls, and providing weather updates and news briefings. These virtual assistants can integrate with smart home devices, such as lights, thermostats, and other home appliances using voice controls.
They can quickly access information from the internet to provide recipes, directions, and answers to a child’s endless questions. Over time, virtual assistants can learn user preferences and use them to tailor their responses and suggestions.
Industry-Specific NLP Chatbots
NLP chatbots are used in almost every industry to increase efficiency and reduce expenses. The following industries, in particular, use chatbots extensively.
- Healthcare: In healthcare, NLP chatbots assist in symptom checking, appointment scheduling, and providing general health advice. They can guide patients through preliminary steps before a doctor’s visit, optimizing the time spent in actual consultations.
- Finance: Banking chatbots help users check account balances, initiate transactions, and provide financial advice. They can also assist in reporting lost cards or fraud, making banking more accessible and secure.
- Travel and hospitality: Chatbots assist with booking flights and hotels and provide travel advice. They offer personalized suggestions, like recommending hotels or activities, based on user preferences.
- Education: Educational chatbots help students in language learning, tutoring, and providing study resources. They answer subject-specific questions, offer learning tips, and even grade some assignments.
- Retail: Retail chatbots improve a customer’s shopping experience by offering product recommendations, assisting in finding items, and answering questions relating to availability, sizes, and more.
Leverage NLP for Your Healthcare Processes
The power of NLP isn’t limited to chatbots. You can use NLP to simplify and automate clinical documentation with tools such as Consensus Clarity CD. This data extraction solution uses NLP to turn unstructured documents such as faxes and other digital scans into structured data that can be sent directly where it needs to be. Your office staff will be freed up to handle high-value tasks such as direct patient care and interaction. You can also increase accuracy by avoiding human errors. Clarity CD puts the power of NLP in your hands, allowing you to quickly process and access critical patient information. Contact us today for a free demo.