One of the popular tasks is the classification of textual data. This includes, for example, sorting reviews into positive and negative categories. This is how spam filtering systems work: they divide emails into spam and non-spam. It also involves intent recognition: the classification of intentions or actions expressed in the text. For instance, determining the user's intent when interacting with a chatbot (information inquiry, product ordering, support, etc.).
NLP enables the development of automatic machine translation systems. This makes it easier to communicate and correspond with friends who have different interests in other countries; this is the principle behind multilingual systems.
Using NLP, an algorithm can be created for the automatic search and extraction of necessary information in texts—such as forming a database based on user registration forms (names, dates, addresses, etc.). Natural language processing is used for automatic text generation—creating articles, news headlines, and answers to questions. This is precisely how the GPT chatbot operates. And, of course, NLP is essential in the development of virtual assistants that interact with users based on oral or written requests.