What is natural language understanding (NLU)?

by Stephen M. Walker II, Co-Founder / CEO

What is natural language understanding?

Natural Language Understanding (NLU) is a subfield of artificial intelligence (AI) and a component of natural language processing (NLP) that focuses on machine reading comprehension. It involves the interpretation and generation of human language by machines. NLU systems are designed to understand the meaning of words, phrases, and the context in which they are used, rather than just processing individual words.

NLU systems typically require a lexicon of the language, a parser, and grammar rules to break sentences into understandable components. They also need a semantic theory to guide comprehension. Advanced applications of NLU attempt to incorporate logical reasoning, usually achieved by mapping the derived meaning into a set of assertions in predicate logic.

NLU is crucial in enabling human-computer interaction by analyzing language versus just words. It allows computers to understand sentiments expressed in natural languages used by humans, such as English, French, or Mandarin, without the formalized syntax of computer languages. It also enables computers to communicate back to humans in their own languages.

NLU is used in a variety of industries and applications, including automated machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis. It's also valuable for technical settings, like online customer service applications and automated systems.

NLU is programmed to understand meaning, despite common human errors, such as mispronunciations or transposed letters and words. It deconstructs human speech using trained algorithms until it forms a semantic model or a set of concepts and categories that have established relationships with one another. Two key concepts in natural language processing are intent recognition and entity recognition.

In the context of business, NLU is increasingly vital as it allows organizations to create products or tools that can understand and respond to human language, making it possible to carry out a dialogue with a computer using a human-based language. This is particularly useful for consumer products or device features, such as voice assistants and speech-to-text applications.

How does natural language understanding work?

Natural Language Understanding (NLU) is a critical component of Natural Language Processing (NLP), which is an interdisciplinary subfield of computer science and linguistics concerned with enabling computers to understand and manipulate human language. NLU specifically focuses on the comprehension aspect, analyzing the meaning behind sentences and words within the context they are used.

NLU involves two main components: intent recognition and entity recognition. Intent recognition is about identifying the purpose or goal behind a user's input, such as determining what action a user wants to perform when they provide a command or query. Entity recognition involves identifying and categorizing key elements in the text, such as names of people, places, organizations, dates, and other specific information.

To achieve this understanding, NLU systems employ various techniques, including:

  1. Named Entity Recognition (NER): Extracting entities like names, places, and organizations from text.
  2. Sentiment Analysis: Determining the emotional tone behind a text, categorizing it as positive, negative, or neutral.
  3. Text Summarization: Condensing large volumes of text into shorter, coherent summaries.
  4. Aspect Mining: Identifying specific attributes or aspects of a given subject within the text.
  5. Topic Modeling: Discovering the abstract topics that occur in a collection of documents.

NLU systems use computational linguistics, machine learning, and deep learning models to process human language. These systems can handle the complexities of human language, including dialects, slang, and grammatical irregularities. They are used in various applications such as chatbots, voice assistants, customer feedback analysis, and more, enabling machines to understand human language and communicate effectively with users.

The process of NLU typically involves data preprocessing, where text is tokenized into smaller units, and features such as sentence structure and word meanings are extracted. Then, algorithms are applied to interpret the text's meaning and respond appropriately.

What are the benefits of using natural language understanding?

Natural Language Understanding (NLU), a subset of Natural Language Processing (NLP), offers numerous benefits that can significantly enhance business operations, customer experience, and data analysis. Here are some key advantages:

  1. Large-Scale Analysis — NLU allows businesses to process and analyze vast amounts of text data, such as social media posts, customer reviews, and internal documents, in a fraction of the time it would take humans.

  2. Objective and Accurate Analysis — NLU can provide more objective and accurate analyses than manual methods, reducing the risk of human error and bias.

  3. Streamlining Processes and Reducing Costs — By automating tasks like customer service via chatbots, NLU can streamline business operations and reduce costs.

  4. Improved Customer Satisfaction — NLU can enhance customer experience by enabling more natural and efficient interactions with AI-powered services, such as chatbots and virtual assistants.

  5. Market Understanding — NLU can help businesses better understand their market by analyzing customer sentiment and feedback at scale.

  6. Empowering Employees — By automating routine tasks, NLU can free up employees to focus on more complex and strategic tasks.

  7. Real, Actionable Insights — NLU can provide businesses with actionable insights derived from large-scale text analysis, helping them make data-driven decisions.

  8. Multilingual Understanding — NLU can understand several languages, dialects, slang, and jargon, making it useful for businesses operating in multilingual environments.

  9. Fraud Detection — NLU can help identify and address fraudulent claims and behavior.

  10. Improved Data Analysis — NLU can extract pertinent information from unstructured data for improved data analysis.

  11. Automation of Routine Tasks — NLU can automate tasks like answering customer inquiries and processing transactions, reducing the need for human involvement and saving labor costs.

  12. Structuring Company Data — NLU can structure company data, making it easier to analyze and derive insights.

These benefits make NLU a powerful tool for businesses, enabling them to leverage their text data in ways that were previously impossible. As NLU technology continues to advance, its potential applications and benefits are likely to expand even further.

What are some common applications of natural language understanding?

Natural Language Understanding (NLU), a subset of Natural Language Processing (NLP), has a wide range of applications across various industries. Here are some key applications:

  1. Sentiment Analysis — NLU can be used to analyze text data to determine the sentiment behind it. This is particularly useful in areas like customer feedback analysis, social media monitoring, and market research.

  2. Chatbots and Virtual Assistants — NLU is the technology behind chatbots and virtual assistants like Siri, Alexa, and Google Assistant. These tools use NLU to understand and respond to user queries in a conversational manner.

  3. Text Extraction and Classification — NLU can help in extracting relevant information from large volumes of unstructured text data. It can also classify text into predefined categories, which is useful in applications like spam filtering, news categorization, and customer request routing.

  4. Automated Translation — NLU is used in tools like Google Translate to understand and translate text from one language to another.

  5. Customer Experience Enhancement — NLU can help companies analyze customer feedback across various channels, identify trends, and use these insights to improve their products or services.

  6. Automated Ticketing Support — NLU can understand customer requests and facilitate speedy resolution, improving customer service efficiency.

  7. Market Intelligence — NLU can help gather publicly available information quickly on other companies and individuals, providing a competitive edge.

  8. Document Analysis — NLU can be used to review and analyze documents ranging from insurance policies to contracts, saving time and reducing human error.

  9. Interactive Voice Response (IVR) Systems — NLU is used in IVR systems to handle customer queries and complaints on a 24/7 basis without human intervention.

  10. Recruitment — NLU can be used to screen resumes, analyze candidate sentiment during interviews, and automate communication between recruiters and candidates.

These applications are just the tip of the iceberg. As NLU technology continues to evolve, we can expect to see even more innovative uses in the future.

What are some challenges associated with natural language understanding?

Natural Language Understanding (NLU), while a powerful tool, faces several challenges due to the complexity and fluidity of human language. Here are some of the key challenges:

  1. Understanding Context — Words and phrases can have different meanings depending on the context in which they are used. NLU systems must be able to interpret these varying contexts accurately, which is a significant challenge.

  2. Handling Ambiguity — Human language is often filled with words or phrases that may have multiple meanings, leading to ambiguity. NLU systems must be able to handle this ambiguity and interpret the intended meaning.

  3. Dealing with Informal Language — Informal phrases, expressions, idioms, and culture-specific lingo can present problems for NLU, especially as these expressions may have different meanings in different geographic areas and are constantly evolving.

  4. Handling Misspellings and Misused Words — Misspelled or misused words can create problems for text analysis. While autocorrect and grammar correction applications can handle common mistakes, they don't always understand the writer's intention.

  5. Language Differences — There are over 7,000 languages spoken worldwide, and developing NLU systems that can efficiently process and interpret them is a significant challenge.

  6. Lack of Sufficient Training Data — Deep learning models for NLU need high-quality training data to work well. However, getting labeled data for training can be complex, particularly for languages with few resources.

  7. Handling Multilingual and Code-Switching Text — Multilingual communication and code-switching (blending many languages in a conversation) are common in human communication, and handling these effectively is a challenge for NLU models.

  8. Keeping a Conversation Moving — Many modern NLU applications are built on dialogue between a human and a machine. The NLU system needs to be able to keep the conversation moving, providing additional questions to collect more information and always pointing toward a solution.

  9. Development Time — The development of NLU systems can be time-consuming due to the complexity of human language and the need for extensive training data.

  10. Phrasing Ambiguities — The way sentences are phrased can lead to ambiguities that NLU systems must be able to interpret correctly.

These challenges highlight the complexity of human language and the difficulties in creating machines that can fully understand and interpret it. However, as NLU technology continues to advance, solutions to these challenges are being developed, bringing us closer to more sophisticated and accurate NLU systems.

More terms

What is action model learning?

Action model learning is a form of inductive reasoning in the field of artificial intelligence (AI), where new knowledge is generated based on an agent's observations. It's a process where a computer system learns how to perform a task by observing another agent performing the same task. This knowledge is usually represented in a logic-based action description language and is used when goals change. After an agent has acted for a while, it can use its accumulated knowledge about actions in the domain to make better decisions.

Read more

Why is Analysis of Algorithms important?

Analysis of algorithms is crucial for understanding their efficiency, performance, and applicability in various problem-solving contexts. It helps developers and researchers make informed decisions about choosing appropriate algorithms for specific tasks, optimizing their implementations, and predicting their behavior under different conditions or inputs.

Read more

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Start for free