NLP is a branch of AI that allows computers to understand, write, and speak languages in the same way that humans do. Search engines, translation services, and chat or voice bots are all examples of NLP applications.
Why Is NLP Important?
NLP models rely on machine learning to understand written or spoken language in the majority of cases. The models learn in the same way that humans do: they use machine learning to collect audio or written terms from humans, interpret the data, and respond in the same language. Some NLP-enabled AI algorithms can outperform humans in over 100 languages when it comes to understanding the meaning of a text or searching for the answer to a random question. The business world will benefit greatly from this level of expertise. No matter what language you speak or how you phrase what you’re looking for, NLP can make searching for a product or service or asking a brand a question quick and easy.
The advantages of NLP
Due to their sizable data pools and available budget for data processing software, large enterprises stand to benefit the most from machine learning and AI. NLP can help businesses of all sizes analyse and process unstructured data of any size, which can be done with NLP.
Enhancing the customer experience:
Due to NLP’s ability to streamline processes, live agents have more time to attend to urgent and complex customer needs. When people ask a lot of the same questions, NLP can suggest answers. It can also search through text for an answer, or even use a chatbot to have conversations. This results in faster response times and happier and more loyal customers.
Assuring that customers’ requirements are met:
Numerous businesses invite customers to participate in surveys regarding a product they purchased or a service they received. Once those are complete, many businesses find it costly and difficult to analyse the data. NLP takes the pain out of parsing through reviews and transforms them into real-world, actionable insights, enabling businesses to adjust their offerings in response to customer feedback in order to increase future customer satisfaction. This responsiveness can occasionally mean the difference between a repeat customer and the need to compete for a new one.
Employee and customer protection:
Phishing attacks are on the rise, and many businesses are finding it difficult to combat them. Some NLP algorithms can tell if a message is likely to be fraudulent, which allows businesses to filter out dangerous messages from their employees’ and customers’ inboxes.
NLP Use Cases
Chatbots are one of the simplest applications of NLP. NLP can assist chatbots in better comprehending and responding to customer inquiries. This enables bots to serve as strategic tools for businesses.
Intelligent search:
The majority of search engines operate solely on the basis of specific keywords rather than a true understanding of the user’s intent. NLP-enabled intelligent search enables users to pose questions, and the engine will pore over documents in search of an answer. Businesses should aim to provide an intelligent search engine.
Automated topic detection:
NLP-powered chatbots can improve the efficiency of conversations by automating skills and detecting topics, allowing them to route the conversation to the appropriate agent if necessary. For instance, NLP can detect the messages and respond appropriately to the customer.
Support via live agent:
Even when a chatbot does not initiate a conversation with a customer, NLP can increase the efficiency of a conversation. NLP can generate response suggestions based on the context of the conversation and the customer, removing the need for live agents to type out individual responses. This can help businesses scale their customer service processes in a timely manner.
Real-World Applications
NLP has a variety of real-world applications and use cases, including the following:
- Siri and Alexa are examples of voice-activated assistants.
- Natural language generation for customer service chatbots to answer questions.
- By scanning through people’s listed skills and experience on sites like LinkedIn, we can streamline the recruiting process.
- Grammarly, for example, uses NLP to assist in correcting errors and making suggestions for simplifying complex writing.
- Language models, such as autocomplete, are trained to anticipate the next words in a text based on the previous ones typed.
Conclusion
NLP is the branch of AI that studies how machines interact with human language. NLP works in the background to improve the tools we use on a daily basis, such as chatbots, spell checkers, and language translators. When combined with machine learning algorithms, NLP enables systems to self-learn and improve. Among other things, NLP-powered tools can help you classify social media posts by their mood or find the names of people in business emails. Moreover, investing in NLP can significantly improve a business’s ability to engage with customers globally. Whether your passion is data science or AI, the world of NLP is constantly providing solutions to real-world problems.
Source: indiaai.gov.in
