NLP vs NLU vs NLG: Whats the difference?

NLU enables human-computer interaction in the sense that as well as being able to convert the human input into a form the computer can understand, the computer is now able to understand the intent of the query. Once the intent is understood, NLU allows the computer to formulate a coherent response to the human input. It enables computers to evaluate and organize unstructured text or speech input in a meaningful way that is equivalent to both spoken and written human language. The computational methods used in machine learning result in a lack of transparency into “what” and “how” the machines learn. This creates a black box where data goes in, decisions go out, and there is limited visibility into how one impacts the other.

The terms NLP and NLU are often used interchangeably, but they have slightly different meanings. Developers need to understand the difference between natural language processing and natural language understanding so they can build successful conversational applications. On our quest to make more robust autonomous machines, it is imperative that we are able to not only process the input in the form of natural language, but also understand the meaning and context—that’s the value of NLU. This enables machines to produce more accurate and appropriate responses during interactions.

What is natural language understanding?

With NLP integrated into an IVR, it becomes a voice bot solution as opposed to a strict, scripted IVR solution. Voice bots allow direct, contextual interaction with the computer software via NLP technology, allowing the Voice bot to understand and respond with a relevant answer to a non-scripted question. NLU is particularly effective with homonyms – words spelled the same but with different meanings, such as ‘bank’ – meaning a financial institution – and ‘bank’ – representing a river bank, for example. Human speech is complex, so the ability to interpret context from a string of words is hugely important. By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Since it is not a standardized conversation, NLU capabilities are required.

If you give an idea to an NLG system, the system synthesizes and transforms that idea into a sentence. It uses a combinatorial process of analytic output and contextualized outputs to complete these tasks. Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write. NLG is the process of producing a human language text response based on some data input.

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Latin, English, Spanish, and many other spoken languages are all languages that evolved naturally over time. As businesses adopt NLP and NLU strategically, they can unlock a in the data-driven landscape of the future. To harness the full potential of these technologies and embark on your AI journey, talk to our experts at Softweb Solutions. NLG is also expected to see advancements in the area of content creation.

As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data. The two most common approaches are machine learning and symbolic or knowledge-based AI, but organizations are increasingly using a hybrid approach to take advantage of the best capabilities that each has to offer. It takes data from a search result, for example, and turns it into understandable language. So whenever you ask your smart device, “What’s it like on I-93 right now?

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Conversely, NLU aims to comprehend the meaning of sentences, whereas NLG focuses on formulating correct sentences with the right intent in specific languages based on the data set. Refer to our Shaip experts to learn about these technologies in detail. For machines, human language, also referred to as natural language, is how humans communicate—most often in the form of text.

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