NLP vs NLU vs. NLG: Understanding Chatbot AI

NLG is used to generate a semantic understanding of the original document and create a summary through text abstraction or text extraction. In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content. Text abstraction, the original document is phrased in a linguistic way, text interpreted and described using new concepts, but the same information content is maintained. NLU is used in a variety of applications, including virtual assistants, chatbots, and voice assistants. These systems use NLU to understand the user’s input and generate a response that is tailored to their needs.

NLG, on the other hand, involves using algorithms to generate human-like language in response to specific prompts. As the name suggests, the initial goal of NLP is language processing and manipulation. It focuses on the interactions between computers and individuals, with the goal of enabling machines to understand, interpret, and generate natural language.

Best Use Cases of Natural Language Processing (NLP)

The ultimate of NLP is to read, decipher, understand, and make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text. In this case, NLU can help the machine understand the contents nlp vs nlu of these posts, create customer service tickets, and route these tickets to the relevant departments. This intelligent robotic assistant can also learn from past customer conversations and use this information to improve future responses. It’s easier to define such a branch of computer science as natural language understanding when opposing it to a better known-of and buzzwordy natural language processing.

NLG is used in a variety of applications, including chatbots, virtual assistants, and content creation tools. For example, an NLG system might be used to generate product descriptions for an e-commerce website or to create personalized email marketing campaigns. A Large Language Model (LLM) is an advanced artificial intelligence system that processes and generates human language.

The difference between Natural Language Processing (NLP) and Natural Language Understanding (NLU)

This is in contrast to NLU, which applies grammar rules (among other techniques) to “understand” the meaning conveyed in the text. In order for systems to transform data into knowledge and insight that businesses can use for decision-making, process efficiency and more, machines need a deep understanding of text, and therefore, of natural language. In machine learning (ML) jargon, the series of steps taken are called data pre-processing. The idea is to break down the natural language text into smaller and more manageable chunks.

• Since then, with the help of progress made in the field of AI and specifically in NLP and NLU, we have come very far in this quest.
• This is especially important for model longevity and reusability so that you can adapt your model as data is added or other conditions change.
• This shows the lopsidedness of the syntax-focused analysis and the need for a closer focus on multilevel semantics.
• When we hear or read  something our brain first processes that information and then we understand it.

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. These three terms are often used interchangeably but that’s not completely accurate. Natural language processing (NLP) is actually made up of natural language understanding (NLU) and natural language generation (NLG).

It provides the ability to give instructions to machines in a more easy and efficient manner. 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. Just like learning to read where you first learn the alphabet, then sounds, and eventually words, the transcription of speech has evolved over time with technology.

When it comes to relations between these techs, NLU is perceived as an extension of NLP that provides the foundational techniques and methodologies for language processing. NLU builds upon these foundations and performs deep analysis to understand the meaning and intent behind the language. NLP primarily works on the syntactic and structural aspects of language to understand the grammatical structure of sentences and texts. With the surface-level inspection in focus, these tasks enable the machine to discern the basic framework and elements of language for further processing and structural analysis. As NLP algorithms become more sophisticated, chatbots and virtual assistants are providing seamless and natural interactions.

It involves techniques like sentiment analysis, named entity recognition, and coreference resolution. NLU is the ability of a machine to understand and process the meaning of speech or text presented in a natural language, that is, the capability to make sense of natural language. To interpret a text and understand its meaning, NLU must first learn its context, semantics, sentiment, intent, and syntax. Semantics and syntax are of utmost significance in helping check the grammar and meaning of a text, respectively.

• As a result, they do not require both excellent NLU skills and intent recognition.
• Questionnaires about people’s habits and health problems are insightful while making diagnoses.
• It enables machines to understand, generate, and interact with human language, opening up possibilities for applications such as chatbots, virtual assistants, automated report generation, and more.
• NLU enables machines to understand and interpret human language, while NLG allows machines to communicate back in a way that is more natural and user-friendly.
• In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content.

Next, the sentiment analysis model labels each sentence or paragraph based on its sentiment polarity. NLG systems enable computers to automatically generate natural language text, mimicking the way humans naturally communicate — a departure from traditional computer-generated text. When given a natural language input, NLU splits that input into individual words — called tokens — which include punctuation and other symbols. The tokens are run through a dictionary that can identify a word and its part of speech. The tokens are then analyzed for their grammatical structure, including the word’s role and different possible ambiguities in meaning.

How NLP is Changing the Way We Interact with Computers

Figure 4 depicts our sample of 5 use cases in which businesses should favor NLP over NLU or vice versa. The future of NLP, NLU, and NLG is very promising, with many advancements in these technologies already being made and many more expected in the future. Hiren is VP of Technology at Simform with an extensive experience in helping enterprises and startups streamline their business performance through data-driven innovation.

Latin, English, Spanish, and many other spoken languages are all languages that evolved naturally over time. The aim is to analyze and understand a need expressed naturally by a human and be able to respond to it. Our open source conversational AI platform includes NLU, and you can customize your pipeline in a modular way to extend the built-in functionality of Rasa’s NLU models.

The Success of Any Natural Language Technology Depends on AI

And if the assistant doesn’t understand what the user means, it won’t respond appropriately or at all in some cases. Conversational interfaces are powered primarily by natural language processing (NLP), and a key subset of NLP is natural language understanding (NLU). 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.