What is NLP and NLU
Natural Language Processing is this exciting multidisciplinary field that enables people to communicate with machines in everyday life. This enables them to help us complete simple tasks. It combines the methods of computer science, artificial intelligence, linguistics and data science and uses machine learning. NLU uses this to perform tasks such as answering questions, summarizing and translating texts and documents. We all encounter NLP in our everyday lives when we interact with chatbots, use language assistants or utilize translation tools.
Natural Language Understanding or NLU is a subset of NLP, which is the semantic analysis of given text or language to determine its meaning. In contrast to conventional keyword-based search systems, NLU helps software to understand the intention behind the user’s input. Even if the language is complex, unstructured or colloquial.
In short: NLU takes unstructured data and structures it so that machines can work with it.
NLU in the virtual agent
By using NLU, the virtual agent can go beyond rigid, rule-based responses and have more natural and fluid conversations with users.
The virtual agent uses the following tools to improve interactions with it.
- Entity extraction
NLU not only understands what a user wants to achieve, but also enables the virtual agent to extract specific details or entities from the user’s input. For example, in a sentence such as “I need to reset my password for the HR system”, NLU can identify “reset password” as an action and “HR system” as an entity. This allows the virtual agent to provide precise, contextual support. - Multi-turn conversations
ServiceNow’s NLU-enhanced virtual agent supports multi-turn conversations, meaning it can handle multiple back-and-forth interactions without losing context. For example, if a user asks for help setting up a VPN and then inquires about configuring a specific device, the virtual agent can keep the conversation flowing and provide tailored advice based on the previous conversations. - Dealing with clarifications
If a user request is ambiguous or unclear, the virtual agent can use NLU to ask clarifying questions. For example, if a user says, “I need help with an account issue,” the virtual agent could respond, “Are you having trouble logging in or is this related to account billing?” This ensures that the user receives more relevant help and avoids frustrating the user with misunderstandings. - Continuous learning and improvement
One of the key benefits of NLU in ServiceNow’s virtual agent is that it continuously learns from interactions. By analyzing conversations over time, the NLU models improve their ability to predict intent, extract entities and provide more accurate responses. This self-improving capability ensures that the virtual agent better understands user requirements and provides better solutions with every interaction.
By equipping ServiceNow’s Virtual Agent with NLU, organizations can transform their IT and customer service landscapes, reducing the burden on support teams while improving the overall user experience.
