What is the Difference Between NLP, NLU, and NLG?

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.

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.

NLP, NLU, and NLG

As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills. The “suggested text” feature used in some email programs is an example of NLG, but the most well-known example today is ChatGPT, the generative AI model based on OpenAI’s GPT models, a type of large language model (LLM). Such applications can produce intelligent-sounding, grammatically correct content and write code in response to a user prompt. NLP is a fast-growing study subject in AI, with applications such as translation, summarization, text production, and sentiment analysis. Businesses utilize NLP to fuel an increasing number of applications, both internal and customer-facing, such as detecting insurance fraud, evaluating customer sentiment, and optimising aircraft maintenance.

It enables machines to understand, interpret, and generate human language in a valuable way. The benefits of NLP systems are that they break down text into words and phrases, analyze their context, and perform tasks like sentiment analysis, language translation, and chatbot interactions. Moreover, OpenAI’s advanced language models empower comprehensive text analysis, while LangChain’s specialized NLP solutions enhance data management. A subfield of artificial intelligence and linguistics, NLP provides the advanced language analysis and processing that allows computers to make this unstructured human language data readable by machines. It can use many different methods to accomplish this, from tokenization, lemmatization, machine translation and natural language understanding.

Artificial Intelligence vs Machine Learning

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.

It was Alan Turing who performed the Turing test to know if machines are intelligent enough or not. It is easy to confuse common terminology in the fast-moving world of machine learning. For example, the term NLU is often believed to be interchangeable with the term NLP. But NLU is actually a subset of the wider world of NLP (albeit an important and challenging subset). Machines programmed with NGL help in generating new texts in addition to the already processed natural language. They are so advanced and innovative that they appear as if a real human being has written them.

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?

NLU algorithms often operate on text that has already been standardized by text pre-processing steps. But before any of this natural language processing can happen, the text needs to be standardized. Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service.

What is NLG?

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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