NLP vs. NLU vs. NLG: what you need to know

Intro

NLU and NLP - how often have we heard this pair of words in recent years? What does the NLU NLP combo mean for the Generative AI industry at all? How do they compare with each other? We have already written about the main differences between Natural Language Processing and Large Language Models, so now it is time to discuss the similarities and differences between NLP NLG NLU.

Why is it so important to talk about this? The point is that according to Markets Insider research, the global natural language processing (NLP) market is expected to be worth USD 34.80 billion by 2025, registering a CAGR of 21.5% during 2020-2025. And these are huge numbers for the AI business world.

Moreover, natural language understanding and processing aim to eventually dominate human-to-machine interaction to the point where talking to a machine is as easy as talking to a human. At the same time, NLG will continue to harness unstructured data and make it more meaningful to a machine.

According to the recent IDC report, the amount of analyzed data “touched” by cognitive systems will grow by a factor of 100 to 1.4 ZB by 2025, impacting thousands of industries and companies around the globe. Recruiting, robotics, healthcare, financial services, customer experience, and education are just a handful of the sectors that will continue to be advanced by NLP, and NLU.

So, let’s dive deeper into these categories and try to find out why they are so popular and important for the global AI market today.

NLP NLU NLG - what are they?

Before starting to talk about the difference between NLP and NLG, NLP and NLU, etc., let’s figure out what conversation language understanding (CLU) is, also well-known as conversational language understanding. It will be easier for us to understand other types later on.

Conversation Language Understanding is a big part of AI understanding natural language field. CLU refers to the ability of a system to comprehend and interpret human language within the context of a conversation. This involves understanding not only the individual words and phrases being used but also the underlying meaning and intent conveyed through natural language.

CLU typically employs various techniques from natural language processing (NLP), machine learning, and artificial intelligence to achieve this understanding. The applications with CLU may use methods such as text parsing, semantic analysis, sentiment analysis, named entity recognition, and context modeling to extract relevant information from the conversation and derive meaning from it.

The goal of CLU is to enable machines to engage in meaningful, contextually appropriate interactions with humans, whether it's in the form of dialogue systems, AI chatbots, virtual assistants, or other applications where understanding human language is essential.

Overall, while NLP provides the foundational techniques and methodologies for processing human language, CLU focuses on applying these techniques to understand and interpret language within the context of a conversation or dialogue, intending to enable more natural and effective communication between humans and machines.

Now, let’s clarify the meanings of the NLP NLG NLU trio.

Natural Language Processing (NLP)

Natural language processing (NLP) is an interdisciplinary field of computer science and information retrieval. NLP has evolved from computational linguistics, drawing upon methodologies from computer science, conversational AI, linguistics, and data science to enable computers to comprehend human language in written and verbal forms. While computational linguistics primarily focuses on the structural aspects of language, NLP places a significant emphasis on employing machine learning and prompt engineering techniques to accomplish tasks such as language translation and question-answering.

The core mechanism of natural language processing involves converting unstructured data into a structured format. This process entails identifying named entities through named entity recognition and discerning word patterns using methods like tokenization, stemming, and lemmatization, which analyze the root forms of words.

There are many NLP algorithms with different approaches customized to specific language tasks. For instance, world-known Hidden Markov Models (HMM) are commonly used for part-of-speech tagging, while recurrent neural networks excel in generating coherent text sequences.

N-grams, a basic language model, assign probabilities to sentences or phrases to predict response accuracy. These techniques synergize to support popular technologies such as chatbots and speech recognition systems like Amazon’s Alexa or Apple’s Siri, with broader applications extending into industries such as education and healthcare.

NLP consists of the following elements:

Text Classification

Text classification involves categorizing textual data into predefined classes or categories. Examples include:

Determining if customer reviews express positive or negative sentiment.
Classifying news articles into topics like sports, politics, or entertainment.
Identifying the language of a given text, such as English, Spanish, or French.

Information Extraction

Information extraction entails identifying and extracting structured information from unstructured text. Examples include:

Creating a structured database of events by extracting names, dates, and locations from text.
Populating a job application form by extracting phone numbers, email addresses, and addresses from a resume.
Extracting details about specific events or incidents mentioned in the text.

Machine Translation

Machine translation automatically translates text from one language to another using computational algorithms. For instance:

Input: “I’m going to the cinema”
Output: “Je vais au cinéma”

Question Answering System

A question-answering system provides automatic answers to user queries based on available information. For example: Answering questions like “What is the capital of France?” with the response “Paris.”

NLP techniques aim to bridge the gap between human language and machine language, enabling computers to process and analyze textual data effectively.

Natural Language Understanding (NLU)

Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subset of natural-language processing in AI understanding natural language that deals with machine reading comprehension.

NLU constitutes a segment within the realm of natural language processing. It engages in syntactic and semantic analysis of both text and speech to decipher the meaning embedded within a sentence. Syntax pertains to the grammatical structure of a sentence, while semantics delves into its intended significance. NLI also establishes an ontology, a structured framework delineating the interrelations among words and phrases.

While humans instinctively perform such analyses during conversations, machines require a fusion of these analytical processes to grasp the intended meaning across diverse texts.

Natural language understanding includes a range of components. Let’s have a look at them.

Syntactic Parsing

Syntactic parsing involves analyzing the grammatical structure of a sentence to discern the relationships between words and their respective roles.

Semantic Analysis

Semantic analysis delves into understanding the meaning and interpretation of text by considering contextual cues and word relationships.

Sentiment Analysis

Sentiment analysis involves determining the emotional tone expressed in text, aiding tasks like:

Identifying joy, sadness, or anger in tweets. Evaluating whether a movie review conveys a positive or negative opinion.
Assisting in market research by analyzing customer sentiments on products or market trends.

Named Entity Recognition (NER)

NER entails identifying and categorizing named entities such as people, organizations, locations, and dates within the text.

Overall, NLU helps the machines like chatbots to understand the data. It is used to interpret data to understand the meaning of data to be processed accordingly and solves it by understanding the text's context, semantics, syntax, intent, and sentiment. For this purpose, various rules, techniques, and models are used.

Natural Language Generation (NLG)

Natural language generation (NLG) is a process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information".

NLG constitutes another facet of natural language processing and conversation language understanding, complementing the domain of natural language understanding. While NLU focuses on enhancing computer reading comprehension, NLG empowers computers to generate written content. It involves the process of producing human language text responses based on input data, which can further be converted into speech format through text-to-speech or even text-to-video services.

The key components of NLG are the following:

Text Planning

In the realm of NLG, text planning stands as a pivotal stage where the structure, sequence, and coherence of the resulting text are meticulously crafted. Here are some samples:

Structuring the introduction, main arguments, and conclusion of a persuasive essay to weave a cohesive narrative.
Sequencing the steps in a recipe in a clear and logical manner to facilitate the reader's progression through the cooking process.

Data-To-Text Transformation

Data-to-text transformation involves the conversion of structured data into natural language narratives. For example, it can be:

Generating a weather report by translating meteorological data, encompassing temperature, humidity, and precipitation, into a descriptive overview of prevailing weather conditions.
Crafting a sales report by transforming sales metrics, trends, and analytical insights into a narrative summary elucidating the company’s performance.

Surface Realization

Surface realization represents the final step in NLG, encompassing the generation of the text's ultimate form, including grammar, vocabulary choice, and linguistic nuances. Here are some examples:

Rendering a structured expression such as “I will go to the store” into its surface equivalent “I’m going to the store.”
Adapting a logical statement like “John eats apples” into its surface manifestation “John is eating apples.”

NLG also encompasses text summarization capabilities, allowing the generation of concise summaries from input documents while preserving the essence of the information. Initially, NLG systems relied on templates for text generation.

However, with advancements in technology, NLG systems have evolved significantly. Techniques such as recurrent neural networks, and transformer models have been employed to facilitate dynamic and real-time text generation, marking a notable shift in conversational language understanding.

NLU vs NLP vs NLG: what's the difference?

So, as far as we have already examined the core of a trio called NLP NLG NLU, let’s now try to find out the main difference between NLP NLU and NLG.

While NLU focuses on comprehending human language, interpreting meaning, and extracting relevant information from textual or spoken input. NLP encompasses a wider range of language-related tasks, including NLU, but extends beyond to cover processes like text summarization, sentiment analysis, and machine translation. NLP serves as a comprehensive framework for processing and analyzing natural language data, facilitating tasks such as information retrieval, question answering, and dialogue systems, usually used in AI Assistants.

On the other hand, NLG involves the generation of human-like language output based on structured or unstructured data input. Unlike NLU and NLP, which focus on understanding and processing existing language, NLG is concerned with producing coherent and contextually appropriate textual or verbal responses. It encompasses tasks such as text planning, data-to-text transformation, and surface realization to generate narratives, reports, or dialogue responses. In essence, while NLU deals with understanding language, NLP covers a broader spectrum of language processing tasks, and NLG centers on generating language output.

To better understand the difference between NLP and NLG and NLU, let’s have a look at the table below:

So, now we may break down the differences between NLP NLU, and NLG:

NLP (Natural Language Processing):

Acts as a crucial bridge between human language and machines.
Enables machines to understand, interpret, and generate human language.
Facilitates effective communication and interaction with users.

NLU (Natural Language Understanding):

Focuses on comprehending language input.
Enables machines to understand and interpret the meaning and intent behind text or speech.

NLG (Natural Language Generation):

Concentrate on generating human-like language.
Allows computers to produce coherent and contextually appropriate text or speech output.

Overall, all these components form the core elements for developing AI-based systems, including AI chatbots, AI assistants, and automated content-generation tools. While NLU enables meaningful interpretation, NLP processes and analyzes language data, and NLG facilitates the generation of language output, completing the cycle of human-machine interaction.

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Applications of NLP, NLU, and NLG

In this part of our article let’s dive deeper into the applications of NLP NLG and NLU. We have already mentioned them in our previous comparison table but now we suggest to examine each of them in detail.

NLP Applications

Applications of Natural Language Processing can be used in absolutely different industries and domains, such as education, eCommerce, healthcare, human resources, and many other fields. Let’s have a look at particular applications.

Information Retrieval and Search Engines. NLP technologies power search engines to understand user queries and retrieve relevant information from vast databases or the web.
Text Classification and Categorization. NLP is used to classify textual data into predefined categories, such as spam detection in emails or sentiment analysis in social media.
Machine Translation. NLP facilitates the automatic translation of text from one language to another, enabling cross-lingual communication in various applications.
Speech Recognition. NLP techniques are used in converting spoken language into text, enabling voice commands in virtual assistants and transcription services.

Text Summarization. NLP algorithms generate concise summaries from lengthy documents, aiding in information extraction and content summarization.

NLU Applications

The applications of Natural Language Understanding enable systems to comprehend and interpret human language. They are usually introduced in such systems as question answering, sentiment analysis, chatbot interaction, virtual assistant capabilities, and document understanding. Here are some of the examples.

Question Answering Systems. NLU enables systems to comprehend user questions and provide accurate answers by understanding the semantics and context of the query.
Sentiment Analysis. NLU techniques analyze text to determine the sentiment or emotional tone expressed, facilitating brand monitoring, customer feedback analysis, and market research.
Intent Recognition in Chatbots (в ідеалі тут лінку дати на статтю про Intent in chatbots..). NLU powers chatbots to understand user intents and respond appropriately, improving user experience in conversational interfaces.
Language Understanding in AI Assistants. NLU enables virtual assistants to understand user commands and queries and perform tasks like setting reminders, making appointments with AI, and providing personalized recommendations.

Document Understanding. NLU algorithms extract key information from documents, enabling systems to understand and process unstructured textual data more effectively.

NLG Applications

The applications of Natural Language Generation (NLG) include automated report generation, chatbot responses, content creation, personalized recommendations, and language translation output, enhancing various domains such as recruiting, customer service, data analysis, and marketing. Let’s have a look at them deeply.

Content Generation. NLG systems generate human-like text for various applications, including news article generation, report writing, and personalized product descriptions.
AI Chatbot Responses. NLG produces conversational responses in chatbots, enabling them to interact naturally with users and provide relevant information or assistance.
Automatic Report Generation. NLG automates the process of generating reports from structured data, producing summaries, insights, and analysis in a readable format.
Personalized Recommendations. NLG generates personalized recommendations based on user preferences, browsing history, or past interactions, providing users with the recommended decisions.

Language Translation Output. NLG converts machine-translated text into natural-sounding language, improving the readability and fluency of translated content.

These applications demonstrate the versatility and utility of NLP, NLU, and NLG across various domains, revolutionizing the way we interact with technology and process textual information.

Examples of NLP, NLU, and NLG

So, now it is time to discover exact examples of NLP, NLG, and NLU. The examples of these categories can be well-known companies and enterprises but it doesn’t mean that they can’t be created by a small startup. Let’s see more.

Google

Google is one of the best examples of both NLP NLU and NLG. They have multiple products that use top-notch technologies in the field of Generative AI tech. Here are some ways Google uses NLP, NLG, and NLU.

Google Search. Google's search engine uses NLU algorithms to understand search queries and deliver relevant search results. NLP helps Google interpret user intent and context to provide more accurate and useful search results.
Google Translate. Google Translate uses NLP and NLG technologies to translate text between different languages. It leverages machine learning models to understand the semantics of sentences and generate accurate translations.
Google Assistant. Google Assistant uses natural language understanding, enabling users to interact with it conversationally. Users can ask questions, make requests, and carry out tasks using natural language commands, and Google Assistant uses NLP to comprehend and respond appropriately.
Google Ads. NLP and NLG are used in Google Ads to analyze and understand the content of advertisements and match them with relevant search queries. This helps advertisers reach their target audience more effectively by displaying contextually relevant ads.
Google Maps. NLP is used in Google Maps for various features such as voice-guided navigation and location-based search. Users can ask for directions, search for nearby places, and interact with Maps using natural language commands.
Gmail. Gmail employs NLP for features like Smart Reply and Smart Compose. Smart Reply suggests quick responses to emails based on the content, while Smart Compose assists users in writing emails by predicting the text as they type.
Google News. NLP is used in Google News to provide personalized news recommendations based on user interests and preferences. The system analyzes articles and categorizes them to deliver relevant news content to users.

Siri

Apple’s Siri - is a great Natural Language Understanding example. This virtual assistant uses both NLU and NLP to comprehend and respond to user commands and queries effectively. Let’s have a look.

Intent Recognition. Siri’s NLP algorithms help to recognize the intent behind user commands or queries. For example, when a user says "Set an alarm for 7 AM," Siri understands the intent is to set an alarm.
Entity Extraction. NLU helps Siri extract relevant entities or parameters from user input. In the command "Text John that I'm running late," Siri identifies "John" as the recipient and "I'm running late" as the message content.
Contextual Understanding. Siri's capabilities enable it to understand the context of a conversation. It can interpret follow-up questions or commands based on previous interactions. For instance, if a user asks, "What's the weather like today?" followed by "How about tomorrow?" Siri understands the context of the second question.
Speech Recognition. Moreover, Siri uses NLP to transcribe spoken language into text. It converts the user's voice commands or queries into readable text for further processing.
Language Understanding. Siri’s NLP technologies interpret user queries accurately, even if they are phrased differently or contain natural language variations.
Response Generation. Once Siri understands the user's intent, NLP helps generate appropriate responses. Siri's responses are dynamically generated based on the input, context, and available information, providing relevant and helpful information to the user.

Grammarly

Grammarly is a popular writing assistant tool, that provides users with real-time feedback on their writing. Here's how Grammarly uses NLP, NL, and NLU technologies:

Text Analysis. Grammarly's algorithms analyze text input to identify grammatical errors, punctuation mistakes, and stylistic issues.
Contextual Understanding. NLP enables Grammarly to understand the context of the text and provide suggestions tailored to the writer's intended meaning.
Language Modeling. Grammarly's NLP models use language modeling techniques to predict the next word or phrase in a sentence, helping users compose coherent and fluent text.
Intent Recognition. Grammarly's NLU capabilities allow it to recognize the user's writing goals and preferences, such as tone, formality, and audience.
Feedback Generation. Grammarly generates feedback messages and suggestions in natural language to communicate to the user what changes are needed to improve their writing.
Sentence Rewriting. In some cases, Grammarly may suggest alternative sentence structures or phrasing using NLG techniques to enhance readability and style.
Explanation Generation. When Grammarly detects an error or issue in the text, it generates explanations or suggestions in natural language to help users understand why a particular change is recommended and how it improves their writing.

Despite many extremely famous NLP, NLG, and NLU products, we can still meet hundreds of useful examples.

ELAI

ELAI is one of Springs’ startups that uses NLU NLP technology as a core component of its online text-to-video platform. Here's how it works.

Text-to-Video Conversion. Elai's platform uses NLP algorithms to analyze and process text input provided by users. This text may include script content, instructions, or dialogue for the video presentation.
Language Translation. With NLG’s help, Elai can support over 65 languages for translation. This functionality allows users to create videos in multiple languages, catering to diverse audiences and markets.
Content Customization. Both NLU and NLP enable Elai.io's platform to understand the structure and context of the text input, allowing for fully customizable video slides. Users can add text, speech, media, animations, and more, with NLP ensuring accurate interpretation and incorporation of these elements into the video.
Personalization. Elai platform can provide personalized content creation experiences. The system can customize video presentations based on user preferences, industry-specific terminology, and desired messaging, enhancing engagement and relevance.
Automated Video Creation. NLP plays a key role in automating the video creation process. Elai.io's platform streamlines the creation of videos by generating synthetic videos with digital presenters directly from text input. This automation saves time and resources for companies, making video production more efficient.

Overall, ELAI fully uses the capabilities of NLP to transform text-based content into engaging and customizable video presentations. Moreover, using NLG technology helps the startup’s users to create professional-quality videos quickly and cost-effectively. So, this is another great natural language understanding example.

IONI

Another NLP-based startup created by the Springs team - is our AI Chatbot. IONI is a smart chatbot based on the latest NLP technologies, that talks like a human and creates CTA for your customers. Let’s have a look at how NLP, NLU, and NLG are presented in this product.

Automated Responses. IONI is powered by ChatGPT-4 API, an advanced LLM API, that automatically generates responses to customer support inquiries. By understanding the context and intent of customer messages, IONI provides relevant and accurate responses from its vector knowledge Base.
Knowledge Base Management. With NLU capabilities, IONI allows users to add, search, and edit data in the Knowledge Base. This includes uploading data through various formats such as URLs, HTML, or plain text. NLG helps in organizing and structuring the information effectively for efficient retrieval and usage.
Conversational Analysis. IONI's Conversations feature enables users to track and analyze chatbot interactions with customers. NLP assists in analyzing the responses provided by the chatbot, identifying patterns, and evaluating the effectiveness of the answers. This helps in refining the chatbot's performance and improving customer satisfaction.
Lead Generation. In cases where the AI chatbot cannot find the required information in the Knowledge Base, IONI uses NLG to guide the conversation toward lead collection. By prompting users to provide their contact details or book a call with the support team, IONI captures leads effectively, enhancing customer engagement and conversion opportunities.

Overall, IONI technologies help to automate customer support interactions, improve response quality, and streamline lead generation processes. By using NLP and NLU, IONI enhances the efficiency of customer support teams and elevates the overall customer experience. Feel free to learn more about how this AI chatbot was developed.

NLP vs NLU vs NLG summary and future

So, we have already viewed the best examples of NLU, NLP, and NLG. Now we need to move to the question: is there a future for AI understanding natural language? To answer this question, let’s see the forecast for the next years.

According to Markets and Markets research, the global NLP market is projected to grow from $19 billion in 2024 to $68 billion by 2028, which is almost 3.5 times growth. From 2024 to 2028, we can expect significant advancements and developments in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG). Let’s summarize some of the insights.

Advancements in NLP Models. We can expect the continued evolution of NLP models, with the development of more efficient architectures capable of handling complex language tasks. This includes advancements in transformer-based models, such as ChatGPT, leading to better language understanding and generation capabilities.

Multimodal NLP. There will likely be a shift towards multimodal NLP, which integrates text with other modalities such as images, audio, and video. This will enable AI apps to understand and generate content across different mediums, enhancing their ability to comprehend and communicate in diverse contexts.

NLG for Personalized Content Creation. NLG technologies will continue to improve, allowing for the generation of highly personalized content customized to individual preferences and needs. This includes the development of more sophisticated content generation algorithms, capable of producing diverse forms of content, at the same time lowering costs for AI development.

Ethical and Responsible AI. As AI technologies become more pervasive in society, there will be a growing emphasis on ethical AI standards in the development of NLP-based chatbots. This includes addressing biases in language models, ensuring transparency and accountability in AI decision-making, and promoting fairness and inclusivity in language processing tasks.

Integration with Industry-Specific Applications. NLP, NLU, and NLG technologies will increasingly be integrated into industry-specific applications and domains, such as healthcare, finance, education, and human resources.

Overall, the future is expected to witness rapid advancements in NLP, NLU, and NLG technologies, driving innovation across various domains and reshaping the way humans interact with LLM applications. These advancements hold the potential to revolutionize communication, decision-making, and information processing in diverse contexts, paving the way for a more intelligent AI future.