Generative AI

Generative AI for natural language understanding: Advances in NLU

We interact with machines and computers every day, but how can computers understand the complexities of human language? The answer lies in AI natural language processing.

Language is a very powerful tool, giving people the ability to express themselves. This information can provide great value to modern businesses, allowing them to understand their customers and refine their strategies for competitive advantage. Natural language understanding in AI can help businesses recognise the intent and sentiments behind the information their customers put online and use it to tailor a personalised customer experience.

What is natural language understanding?

In the simplest terms, Natural Language Understanding (NLU) is an AI system that can allow computers to understand the intent and sentiment of a natural language. Instead of relying on a formal computer language context, NLU can help computers comprehend and respond to human language. Generative AI platforms can help businesses build chat or voice-enabled bots that can understand, respond to, and effectively interact with their customers without constant supervision.[1]

How does natural language understanding work?

The two fundamental elements of natural language understanding are intent (sentiment) recognition and entity (named entities and numeric entities) recognition. As intent recognition helps establish the meaning of the text, entity recognition can help establish context to understand what your customers want.

Once you have the natural language input, the NLU process breaks down into three stages, namely:

Tokenisation

The first step is to split the natural language input into individual tokens (words, punctuations, and symbols).

Lexical Analysis

The next step is to place the tokens into the dictionary to understand their meaning and determine their part of speech. This step also involves identifying phrases that AI natural language understanding can use to gain more experience (or train) and storing them in a separate database.

Syntactic Analysis

The last step is to analyse the grammatical structure and role of each token or word, which can help identify and potentially overcome any ambiguities between multiple interpretations of the meaning or role of the words.

Importance of natural language understanding in AI

Both generative AI NLP and AI-backed natural language understanding focus on understanding the human language and can be a powerful tool to understand human language when combined. But it is important to note the key differences between the two; where NLP focuses on breaking down human language so a machine can understand it, NLU focuses on language comprehension.

So, why is it important to understand and implement natural language understanding in AI?

• It can help you understand the meaning of a text, giving you access to business-critical information that can help you gain a competitive edge.
• You can gain insights into your customers’ purchase behaviour and journey that can help you tailor your business strategies better.
• Generative AI natural language understanding can help you tailor marketing campaigns that target specific audience segments – by understanding their interest and predicting their needs – for a more personalised experience.

Natural language understanding applications

Once you have the solutions to understand customer sentiment, you can develop strategies and tools to serve your customers better. Here are some applications of natural language understanding that not only help you understand your customers but also interact with them naturally:

• Answering customer calls, understanding their concerns, and directing them to the right resources are some of the everyday examples of NLU. It can help you stay connected 24/7, promptly respond to customers, and free your staff to focus on more critical things.
• NLU can also simplify data capture – ensuring consistent formatting – without customers having to manually enter information in every field.
• NLU, combined with a generative AI platform, can help you interact with customers naturally, creating personalised response based on specific information or query a customer presents.
• Chatbots and virtual assistants also leverage AI natural language understanding to stimulate conversations with users, understand their needs, redirect them to the right resources, and complete basic tasks with minimal interactions with the machine.
• NLU can help simplify user sentiment and intent analysis, pursuing social media posts, comments, and content to understand customers’ sentiments and intent towards your brand.

For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

How can Infosys BPM help?

Infosys Generative AI Business Operations Platform (Infosys Topaz) offers you ready-to-use BPM-focused solutions that can help you leverage generative AI platforms to accelerate value creation for your business. You can re-imagine your business operations for an AI-first digital presence while focusing on reinforcing AI ethics. Implement generative AI natural language understanding to deliver prompt personalised customer service for a competitive advantage with Infosys BPM.