Natural language understanding, or NLU for short, is a core part of artificial intelligence (AI) that allows machines to understand and interpret human language. NLU facilitates machine understanding of speech beyond simple transcription in a way that’s both meaningful and contextually relevant.
In the last few years, NLU has evolved thanks to advancements in machine learning (ML) and deep learning algorithms. These advancements are what have allowed machines to understand the meaning of words and grasp nuances in language like tone, context, and intent. It also enables the function of key NLU components, like semantic and discourse analysis and syntactic parsing.
In this blog post, we’ll examine how recent advancements in AI are transforming the way NLU works, enhancing its accuracy and allowing for more sophisticated language understanding in professional settings.
The Fundamentals of Natural Language Understanding
In many AI systems, natural language understanding acts as the foundation upholding various applications to function, like virtual assistants, chatbots, sentiment analysis tools, and others.
This is achieved through four main components of NLU:
- Syntax: The structure of sentences and how words are combined to form meaning
- Semantics: The meaning of words, phrases, and sentences in a specific context as well as the relationship between words
- Pragmatics: The use of language in context and how it’s influenced by tone, intention, or social context, such as implied meaning or speaker intention
- Discourse: The flow of information involving the understanding of how sentences are connected to form a narrative or dialogue
Aside from these four components that influence NLU systems, this technology is also comprised of algorithms and ML models like neural networks, statistical models, and rule-based systems. These all work together to allow machines to decipher meaning from spoken language, enabling NLU systems to understand context-dependent or even ambiguous speech.
AI and Natural Language Understanding: Applications
NLU has a diverse range of uses and applications in AI programs and can help platforms extract valuable insights from text data. Here are a few common ways AI in natural language understanding is used.
Conversational AI and Virtual Assistants
Popular voice assistants Siri or Alexa use NLU to interpret voice commands and offer relevant information or complete actions. In this use case, NLU is essential for enabling natural interactions between users and AI systems.
Sentiment Analysis and Opinion Mining
NLU can analyze text data to determine a user or writer’s sentiment, which is a valuable tool to better determine general feelings towards a brand, product, or service with large quantities of data coming from customer feedback, social media, and reviews.
Text Summarization and Document Understanding
Long texts or documents can be summarized using NLU technology, which extracts key information. This can help readers quickly understand the content of a large document in various academic or professional settings.
Machine Translation and Multilingual Understanding
NLU is essential for digital translation systems as it helps them understand and translate text from one language to another. As this often also involves capturing meaning or context and translating that to a secondary language, NLU is critical to completing these tasks.
Techniques and Approaches for AI and Natural Language Understanding
There are a variety of techniques and approaches that enable machines to process and understand speech. Let’s take a closer look at some of the core methodologies used in NLU and how they uniquely contribute to the advancement of language understanding in AI systems.
Rule-based NLU Approaches
In NLU, rule-based approaches rely on predefined rules and patterns that can analyze language. Rules are usually created by linguists or experts to identify linguistic features like syntax or semantics and are often used in tools like grammar checkers or some chatbots. These systems are good at handling specific language structures but may struggle with ambiguous languages.
Statistical Methods and Machine Learning Algorithms
Machine learning algorithms and statistical methods are widely used in NLU for sentiment analysis, named entity recognition, or part-of-speech tagging. These approaches can learn patterns from data to better generalize unseen examples and are often used in ML algorithms like Hidden Markov Models (HMM) and decision trees.
Deep Learning Techniques
Deep learning techniques like neural networks have advanced NLU capabilities by enabling them to learn hierarchical representations of language. This facilitates a stronger understanding of complex language patterns and relationships. Transformers, a type of neural network architecture, also play an important role in NLU with models like GPT and BERT excelling in tasks related to language generation, translation, and understanding.
Hybrid Approaches
There are also of course hybrid approaches that combine rule-based and machine learning methods to leverage the strengths of both. An example of this is a system that uses rules to handle basic language structures and then ML for more complex tasks. The combination of both these approaches can improve NLU performance and make these systems more flexible.
Case Studies: Real-World Applications of AI and Natural Language Understanding
AI and natural language understanding have many applications in real-world settings that help make our professional and personal lives easier. To better illustrate how NLU is being applied, let’s take a look at a few examples of well-known companies to assess their individual approaches to using this technology.
Amazon’s Alexa
Amazon uses NLU in its virtual assistant, Alexa, to enable speech-based interactions with its users. People can ask Alexa questions, request information, control smart devices, or trigger tasks like setting reminders or alarms all through voice commands.
Amazon is able to accomplish this by using deep learning techniques, including neural networks and NLU models to understand user queries beyond just vocabulary. For example, Alexa can understand that when you’re asking “Alexa, what’s it like outside?” you’re probably inquiring about the weather though that isn’t explicitly said. Alexa’s voice interpretation capabilities are accomplished by applying AI and various language models to aid in communication between man and machine.
IBM Watson AI
IBM relies on NLU technology in its Watson AI platform, which has various applications in different industries like customer service, finances, and healthcare. As an example, Watson AI can be used in healthcare settings to analyze medical records so doctors can make informed decisions more quickly. As a result of using Watson AI, businesses have experienced a 50% reduction in time spent on information-gathering tasks.
To power Watson AI’s language abilities, IBM uses a combination of rule-based systems, ML algorithms, and natural language processing (NLP) techniques. These approaches all work symbiotically to learn a business’s specific language and jargon, such as understanding medical terminology to extract key patient record data and provide recommendations to healthcare professionals.
Turning Speech Into Action With aiOla
At aiOla, we understand the unique advantages AI and natural language understanding can bring to your organization. Our speech AI uses NLU along with other key technologies like automatic speech recognition (ASR) to bring you the cutting-edge of speech-based technology.
Using aiOla, organizations can collect insights from otherwise lost speech data, turning words into actions and automations to enhance workflows and replace repetitive manual operations. aiOla can understand over 100 different languages in any accent, dialect, or industry jargon, making it a fit for a range of companies, such as fleet management, food manufacturers, and more.
With aiOla, companies have been able to experience a myriad of benefits to their productivity and workflows without disrupting existing workflows.
Thanks to aiOla, companies have:
This increase in productivity and efficiency has helped companies save on cost, resources, and lost time. Not only that but the boost in productivity offered by speech AI can help companies offer better customer service and remain competitive in a constantly evolving market.
Leveraging AI and Natural Language Understanding for Your Business
Introducing new speech AI technology to your organization can be a transformative step that helps future-proof your business and boost productivity. Armed with NLU-powered platforms, your team can leverage the power of speech to influence operations and collect meaningful insights to improve your business. With aiOla, this can all be accomplished with little to no learning curve and minimal disruption to your existing workflows.
Schedule a demo with one of our experts to see how aiOla can help you leverage the power of AI and natural language understanding.
