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NLU has helped organizations across multiple different industries unlock value. For example, insurance organizations can use it to read, understand, and extract data from loss control reports, policies, renewals, and SLIPs. Banking and finance organizations can use NLU to improve customer communication and propose actions like accessing wire transfers, deposits, or bill payments. Life science and pharmaceutical companies have used it for research purposes and to streamline their scientific information management. NLU can be a tremendous asset for organizations across multiple industries by deepening insight into unstructured language data so informed decisions can be made.

NLU is also involved in natural language generation, which involves generating text or speech that sounds natural and coherent to humans. By utilizing NLU techniques, AI systems can generate responses or explanations that are more human-like and easier to understand. Whether you’re on your computer all day or visiting a company page seeking support via a chatbot, it’s likely you’ve interacted with a form of natural language understanding.

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In NLU systems, this output is often generated by computer-generated speech or chat interfaces, which mimic human language the system’s ability to process natural language input. In NLU systems, natural language input is typically in the form of either typed or spoken language. Text input can be entered into dialogue boxes, chat windows, and search engines. Similarly, spoken language can be processed by devices such as smartphones, home assistants, and voice-controlled televisions.

When it comes to customer support, companies utilize NLU in artificially intelligent chatbots and assistants, so that they can triage customer tickets as well as understand customer feedback. Forethought’s own customer support AI uses NLU as part of its comprehension process before categorizing tickets, as well as suggesting answers to customer concerns. There are many downstream NLP tasks relevant to NLU, such as named entity recognition, part-of-speech tagging, and semantic analysis. These tasks help NLU models identify key components of a sentence, including the entities, verbs, and relationships between them. The results of these tasks can be used to generate richer intent-based models. NLU also enables the development of conversational agents and virtual assistants, which rely on natural language input to carry out simple tasks, answer common questions, and provide assistance to customers.

“A Chatbot: A computer program designed to simulate a conversation with human users, especially over the Internet.”

By analyzing customer inquiries and detecting patterns, NLU-powered systems can suggest relevant solutions and offer personalized recommendations, making the customer feel heard and valued. Intent recognition involves identifying the purpose or goal behind an input language, such as the intention of a customer’s chat message. For instance, understanding whether a customer is looking for information, reporting an issue, or making a request. On the other hand, entity recognition involves identifying relevant pieces of information within a language, such as the names of people, organizations, locations, and numeric entities. Domain entity extraction involves sequential tagging, where parts of a sentence are extracted and tagged with domain entities. Basically, the machine reads and understands the text and “learns” the user’s intent based on grammar, context, and sentiment.

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