Understanding and using Natural Language Understanding (NLU)

Exploring the Power and Business Benefits of Natural Language Understanding in AI

What is Natural Language Understanding (NLU) and How is it Used in Practice?

Language — one of the most potent tools humanity has at its disposal. And a key thread for machines to connect with us more profoundly. The field of Natural Language Understanding (NLU) attempts to bridge this gap, allowing machines to comprehend human language better.

Natural language understanding in AI promises a future where machines grasp what humans are saying with nuance and context. AI doesn’t dream, think, or learn as humans do. But NLU can help it understand human speech more accurately. Below we dive deeper into the world of natural language understanding and its applications.

Natural Language Understanding Explained

Natural language understanding (NLU) bestows a computer with the ability to interpret human language. When a computer acquires proficiency in AI-based NLU, it can serve several purposes — think of voice assistants, chatbots, and automated translations.

The most rudimentary application of NLU is parsing — converting text written in natural language into a format structure that machines can understand to execute tasks. For example, NLU would dissect “I am happy” into “I am” and “happy” to help a computer understand it.

However, the domain of natural language understanding isn’t limited to parsing. It encompasses complex tasks such as semantic role labelling, entity recognition, and sentiment analysis.

To fully grasp “what is NLU,” we need to become familiar with NLP (Natural Language Processing) and NLG (Natural Language Generation) concepts.

• NLU takes an input string of text and analyses its meaning.
• NLP is the process of converting human-reachable text into computer-readable data. In today’s world, NLP finds its applications anywhere from chatbots to search engines, understanding user queries to deliver appropriate responses.
• NLG, on the other hand, is the reciprocal of NLP. With NLG, computer-readable data is transformed into human-readable data.

In conjunction with NLP, ML (Machine Learning), and NLG, NLU can construct powerful business solutions.

Why is NLU Imperative, and how does it Operate?

To break it down to its bare bones, NLU takes a natural language input (like a sentence or paragraph) and processes it to produce a sensible output. NLU primarily finds its use cases in consumer-oriented applications like chatbots and search engines where users engage with the system in English or their local language.

Here’s a rough breakdown of how NLU works:

1. Tokenisation: The input is split into distinct words or tokens, including punctuations, symbols, and words from various languages.
2. Lexical analysis: The tokens are placed into a digital lexicon, which includes contextual usage. The lexicon identifies the phrases to store in a database for future reference.
3. Syntactic analysis: The tokens are then assessed for their grammatical composition. The analysis identifies the role each word plays and resolves any ambiguity in the interpretations of said roles.

NLU could be viewed as a minor player compared to machine learning or natural language processing. Still, that would be a colossal underestimation. In fact, NLU is shaping up to be a critical business factor across almost every industry.

The insights gained from NLU analysis could provide crucial business advantages, cutting-edge solutions, and help organisations spot specific patterns in audience behaviour, enabling more effective decision-making.

Why Every Future-oriented Business Should Embrace NLU

• Human language complexity: Human language is multifaceted and filled with nuances. And that’s what makes NLU so critical. NLU empowers technology with human-like understanding levels, even detecting typing or naming errors. These functionalities make NLU beneficial for applications like email responses, chatbot conversations, voice assistants, social media comments, etc.
• Understanding at scale: NLU is dedicated to deriving strategies and techniques to grasp context at scale. With the colossal volumes of unstructured data that businesses generate, NLU can offer priceless insights and holistic responses to your audience’s changing needs.
• Growing NLU usage: The market of NLP, which includes NLU, is estimated to reach over $43b by 2025. Organisations across the globe are already harnessing AI-based NLU to analyse human input and gain actionable insights.

With the increasing number of internet, social media, and mobile users, AI-based NLU has become a common expectation. As 20% of Google search queries are done by voice command, businesses need to understand the importance of NLU for their growth and survival.

To summarise, NLU can not only help businesses comprehend unstructured data but also predict future trends and behaviours based on the patterns observed. In other words, NLU gives you an edge and keeps you ahead in the game.

You’re now equipped with basic NLU know-how. Feel like diving in deeper? Head over to Fast Data Science’s comprehensive guide on NLU to expand your understanding of this fascinating AI domain.