NLP vs NLU vs. NLG: the differences between three natural language processing concepts

Now, consider that this task is even more difficult for machines, which cannot understand human language in its natural form. NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, understanding the input, or generating a response. NLP and NLU are significant terms for designing a machine that can easily understand the human language, whether it contains some common flaws. Meanwhile, NLU is exceptional when building applications requiring a deep understanding of language.

NLU algorithms leverage techniques like semantic analysis, syntactic parsing, and machine learning to extract relevant information from text or speech data and infer the underlying meaning. 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. This text can also be converted into a speech format through text-to-speech services. By combining linguistic rules, statistical models, and machine learning techniques, NLP enables machines to process, understand, and generate human language.

Do You Need Both NLP And NLU?

Within semi-restricted contexts, it can assess the user’s objective and accomplish the required tasks in the form of a self-service interaction. Such a chatbot builds a persona of customer support with immediate responses, zero downtime, round the clock and consistent execution, and multilingual responses. NLP and NLU are technologies that have made virtual communication fast and efficient.

NLP and NLU have unique strengths and applications as mentioned above, but their true power lies in their combined use. Integrating both technologies allows AI systems to process and understand natural language more accurately. To have a clear understanding of these crucial language processing concepts, let’s explore the differences between NLU and NLP by examining their scope, purpose, applicability, and more. Natural language processing and its subsets have numerous practical applications within today’s world, like healthcare diagnoses or online customer service. Such applications can produce intelligent-sounding, grammatically correct content and write code in response to a user prompt.

NLU vs NLP in 2024: Main Differences & Use Cases Comparison

It is a way that enables interaction between a computer and a human in a way like humans do using natural languages like English, French, Hindi etc. In conclusion, NLP, NLU, and NLG are three related but distinct areas of AI that are used in a variety of real-world applications. NLP is focused on processing and analyzing natural language data, while NLU is focused on understanding the meaning of that data. NLG, on the other hand, is focused on generating natural language output.

In summary, NLP comprises the abilities or functionalities of NLP systems for understanding, processing, and generating human language. These capabilities encompass a range of techniques and skills that enable NLP systems to perform various tasks. Some key NLP capabilities include tokenization, part-of-speech tagging, syntactic and semantic analysis, language modeling, and text generation.

Best Use Cases of Natural Language Processing (NLP)

ML algorithms can then examine these to discover relationships, connections, and context between these smaller sections. NLP links Paris to France, Arkansas, and Paris Hilton, as well as France to France and the French national football team. Thus, NLP models can conclude that “Paris is the capital of France” sentence refers to Paris in France rather than Paris Hilton or Paris, Arkansas. Voice assistants equipped with these technologies can interpret voice commands and provide accurate and relevant responses.

Symbolic AI uses human-readable symbols that represent real-world entities or concepts. Logic is applied in the form of an IF-THEN structure embedded into the system by humans, who create the rules. This hard coding of rules can be used to manipulate the understanding of symbols. According to various industry estimates only about 20% of data collected is structured data. The remaining 80% is unstructured data—the majority of which is unstructured text data that’s unusable for traditional methods. Just think of all the online text you consume daily, social media, news, research, product websites, and more.

NLP vs NLU vs NLG: Understanding the Differences

As humans, we can identify such underlying similarities almost effortlessly and respond accordingly. But this is a problem for machines—any algorithm will need the input to be in a set format, and these three sentences vary in their structure and format. And if we decide to code rules for each and every combination of words in any natural language to help a machine understand, then things will get very complicated very quickly. NLP based chatbots not only increase growth and profitability but also elevate customer experience to the next level all the while smoothening the business processes.

• For example, programming languages including C, Java, Python, and many more were created for a specific reason.
• NLU algorithms often operate on text that has already been standardized by text pre-processing steps.
• The tokens are run through a dictionary that can identify a word and its part of speech.
• However, the full potential of NLP cannot be realized without the support of NLU.
• Voice assistants equipped with these technologies can interpret voice commands and provide accurate and relevant responses.
• Chatbots are used by businesses to interact efficiently with their customers.

However, navigating the complexities of natural language processing and natural language understanding can be a challenging task. This is where Simform’s expertise in AI and machine learning development services can help you overcome those challenges and leverage cutting-edge language processing technologies. In addition to natural language understanding, natural language generation is another crucial part of NLP. While NLU is responsible for interpreting human language, NLG focuses on generating human-like language from structured and unstructured data. For machines, human language, also referred to as natural language, is how humans communicate—most often in the form of text. It comprises the majority of enterprise data and includes everything from text contained in email, to PDFs and other document types, chatbot dialog, social media, etc.

These examples are a small percentage of all the uses for natural language understanding. Anything you can think of where you could benefit from understanding what natural language is communicating is likely a domain for NLU. A Voice Assistant is an AI-infused software nlp and nlu entity designed to interpret and respond to voice commands for users interact with through spoken language. We’ve seen that NLP primarily deals with analyzing the language’s structure and form, focusing on aspects like grammar, word formation, and punctuation.

• NLP focuses on processing the text in a literal sense, like what was said.
• The remaining 80% is unstructured data—the majority of which is unstructured text data that’s unusable for traditional methods.
• NLU seeks to identify the underlying intent or purpose behind a given piece of text or speech.
• In the statement “Apple Inc. is headquartered in Cupertino,” NER recognizes “Apple Inc.” as an entity and “Cupertino” as a location.

As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data. For instance, the address of the home a customer wants to cover has an impact on the underwriting process since it has a relationship with burglary risk. NLP-driven machines can automatically extract data from questionnaire forms, and risk can be calculated seamlessly.

Translation

The output transformation is the final step in NLP and involves transforming the processed sentences into a format that machines can easily understand. For example, if we want to use the model for medical purposes, we need to transform it into a format that can be read by computers and interpreted as medical advice. NLP groups together all the technologies that take raw text as input and then produces the desired result such as Natural Language Understanding, a summary or translation. In practical terms, NLP makes it possible to understand what a human being says, to process the data in the message, and to provide a natural language response. In order to be able to work and interact with us properly, machines need to learn through a natural language processing (NLP) system.

For example, in NLU, various ML algorithms are used to identify the sentiment, perform Name Entity Recognition (NER), process semantics, etc. 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. It can identify spelling and grammatical errors and interpret the intended message despite the mistakes. This can have a profound impact on a chatbot’s ability to carry on a successful conversation with a user.