Difference between NLP, NLU and NLG

The importance of NLU and NLP has grown as technology and research have advanced, and computers can now analyze and perform tasks on a wide range of data. But when we talk about human language, the whole picture changes because it is messy and ambiguous. It is more difficult to process human language than statistics.

 

To comprehend human language, the system must comprehend content, sentiment, and purpose. However, understanding human language is critical for understanding the customer's intent in order to run a successful business. 

 

NLU (Natural Language Understanding) and NLP (Natural Language Processing) are crucial in understanding human language in this context. Because they both deal with Natural Language, these terms are sometimes used interchangeably. Their goal is to deal with human language, but they are unable to do so. They are, however, distinct. 

 

Natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all distinct topics. NLU and NLG are, at their most basic, components of NLP.

 

NLU + NLG = NLP

 

Natural Language Understanding (NLU) and Natural Language Generation (NLG), as previously stated, are two subsets of Natural Language Processing (NLP). Although they all deal with natural language, they each play different roles at different points.

 

What is NLP?

 

Natural language processing (NLP), which evolved from computational linguistics, employs methods from a variety of disciplines, including computer science, artificial intelligence, linguistics, and data science, to help computers understand human language in both written and spoken forms.

 

While computational linguistics focuses on aspects of language, natural language processing focuses on the use of machine learning and deep learning techniques to complete tasks such as language translation or question answering.

 

Natural language processing works by converting unstructured data into structured data format. While there are numerous NLP algorithms, different approaches are typically used for different types of language tasks. Hidden Markov chains, for example, are commonly used for part-of-speech tagging.

 

To predict the accuracy of a response, N-grams, a simple language model (LM), assigns probabilities to sentences or phrases.

 

These techniques work together to support popular technology such as chatbots and speech recognition products such as Amazon's Alexa and Apple's Siri. However, its impact has been far-reaching, affecting industries as diverse as education and healthcare.

 

 

What is NLU?

 

Natural language understanding is a subset of natural language processing that determines the meaning of a sentence through syntactic and semantic analysis of text and speech. 

 

Syntax refers to a sentence's grammatical structure, whereas semantics refers to its intended meaning. NLU also creates a relevant ontology, which is a data structure that defines the relationships between words and phrases. 

 

While humans naturally do this in conversation, a machine must combine these analyses in order to understand the intended meaning of various texts. Our ability to distinguish between homonyms and homophones perfectly exemplifies the nuances of language.

 

 

What is NLG?

 

Another subset of natural language processing is natural language generation. Natural language understanding is concerned with computer reading comprehension, whereas natural language generation allows computers to write. 

 

NLG is the process of generating a human language text response from data input. Text-to-speech services can also convert this text into a speech format.
 

NLG also includes text summarization capabilities, which generate summaries from input documents while preserving the information's integrity. Key Point Analysis in That's Debatable is powered by the AI innovation of extractive summarization.

 

Natural language generation (NLG) is the process of using artificial intelligence to convert data into natural language. NLG software accomplishes this by converting numbers into natural language text or speech that humans can understand using AI models powered by machine learning and deep learning.

 

Natural language generation is used by chatbots, voice assistants, and AI blog writers, to name a few. Based on pre-defined templates, NLG systems can convert numbers into narratives. 

 

They can predict which words should be generated next (for example, in an email you're writing). Alternatively, the most advanced systems can generate entire summaries, articles, or responses.

 

Also Read | Natural Language Generation: An Overview

 

Application of NLP, NLU and NLG

 

Applications of NLP

 

1. Immediate Customer Service: Even brands that are unfamiliar with natural language processing should be aware of chatbots and their applications in customer support.  Natural language processing (NLP) has proven to be an extremely effective technology for businesses looking to save time and money while optimizing business processes.

 

2. Saves time and money: Companies can save up to 30% on customer support by using automated chatbots as customer support agents, according to statistics. This is accomplished when NLP models can work with more data, which automatically improves the NLP models' performance and accuracy.

 

3. Moderate use of user-generated content: Users today generate massive amounts of data via digital media. Natural Language Processing (NLP) tools enable businesses to continuously monitor a massive volume of user-generated content across all digital platforms. Businesses use NLP models to maintain the quality of content on forums.

 

4. Increase your conversion rate: Improving conversion rates is frequently the top priority for marketing executives, who employ a variety of strategies to convert visitors to customers. NLP models have been shown to be effective for conversion optimization. As a result, businesses can gain customers' trust and improve conversion rates.

 

Applications of NLU

 

A machine that only understands 0's and 1's may find it difficult to comprehend natural human language. So, what makes it possible? Natural Language Understanding is a concept (NLU). NLU is a subset of machine learning that defines its ability to comprehend and process human language. A few examples of NLU applications are:

 

1. Text Categorization & Classification: NLU enables systems to analyse and categorise textual input (either plain text or STT converted text) based on its content. Among the many applications of text categorization are spam filtration in emails, script compliance, and so on.

 

2. Automatic Summarization: To reduce reading time and avoid information overload, NLU generates compact, fluent summaries from long text documents.

 

3.  Voicebot: Natural language understanding (NLU) enables a voicebot to understand the intent behind the customer's speech and extract important entities from it.

 

4. Question Answering & Semantic Parsing: Question Answering (QA) systems allow machines to automatically answer questions in natural human language. It can function as both a spoken dialogue interface and a text-only system.

 

5. Sentiment Analysis/Emotion Mining: NLU can be useful for Sentiment Analysis by helping to identify and quantify the sentiment underlying an opinion or context, which in turn can be extremely useful in driving purchase decisions.

 

Applications of NLG

 

The NLG market is expanding as a result of the increased use of chatbots, the evolution of messaging from manual to automated messaging, and the increased use of technology involving language or speech.

 

1. NLG bridges the gap between organizations and analysts by providing contextual understanding for data through storytelling and guiding businesses to superior decision-making.

 

2. It enables non-data experts to benefit from the free flow of vast amounts of data and make informed decisions that were previously based primarily on experience and intuition.

 

3. Narratives can be created in multiple languages for people at all levels of an organization's hierarchy.

 

4. Finance, Human Resources, Legal, Marketing and Sales, and Operations can all benefit from NLG.

 

5. This technology has the potential to greatly benefit industries such as telecommunications and information technology, media and entertainment, manufacturing, healthcare and life sciences, government, and defense.

 

6. Some of the most common NLG applications are written analysis for BI dashboards, automated report writing, content creation (Robo journalism), data analysis, personalized customer communications, and so on.

 

Difference between NLP, NLU and NLG

 

Below is the comparison table regarding NLP, NLU, NLG :

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NLP	NLU	NLG
Alan Turing pioneered it in order for a machine to understand the context of any document rather than simply treating it as a collection of words.	This investigates the methods by which computers can understand instructions given to them in human languages such as English, Hindi, and so on.	This enables computers to produce output after comprehending user input in natural languages such as English, Hindi, and so on.
It first appeared around 1950.	This concept first appeared around 1866.	It first appeared around 1960.
NLP is a broad concept.	NLU is a wider concept.	NLG is a narrow concept.
However, if we want something more than understanding, such as decision making, NLP comes into play.	If we are only discussing an understanding text, then NLU suffices.	Based on the structured data, it generates text in a human-like manner.
It is divided into five stages: lexical analysis, syntax analysis, semantic analysis, disclosure integration, and pragmatic analysis.	It is divided into three stages: paraphrasing the input information, text conversion to other languages, and drawing inferences from the given information.	It also has three phases: understanding the information, formulating ways to provide output, and achieving the realisation of providing output in natural languages.
However, when we talk about NLP, we are talking about how the machine processes the given data.    Make decisions, take actions, and respond to the system, for example. It contains the entire process from beginning to end. NLP does not always need to include NLU.	What is written or said is not always intended to be the same. There may be flaws and errors.   NLU ensures that it will infer correct intent and meaning even if data is spoken or written incorrectly. It refers to the ability to comprehend the text	Although NLU generates structured data, the generated text is not always easy for humans to understand. As a result, NLG ensures that it is understandable by humans.
NLP applications include smart assistance, language translation, text analysis, and so on.	NLU applications include speech recognition, sentiment analysis, spam filtering, and so on.	NLG applications include chatbots, voice assistants, and so on.
It takes input from sensors, processes data through various layers, and then outputs.	Sensors and processors are used to collect and process data.	Actuators are used to provide output after understanding and processing.
It converts natural language instructions to computer language, and then the computer returns the information in natural language after processing.	Converts the user's unstructured data into structured or meaningful information.	It provides the user with structured data.
It employs a learning mechanism to produce effective results.	For understanding, it first converts natural language to machine language.	It creates the strategy for text utterance.

 

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Also Read | NLP vs NLU

 

Future of NLP, NLU and NLG

 

Developers concentrate on a few key terms in order to prepare a human language AI system to pass the Turing test. If we represent the entire End to End process mathematically, it contains the following terms:

 

An NLP system is created by combining NLU and NLG.

 

Natural Language Processing (NLP): It comprehends the meaning of the text.

 

NLU (Natural Language Understanding): NLP handles entire processes such as decisions and actions.

 

NLG (Natural Language Generation): This method generates human language text from structured data generated by the system in order to respond.

 

To gain a better understanding of how they are used Consider the following scenario: you have a website where you must post stock market reports every day. You must research and collect text for this task on a daily basis, create reports, and post them on a website.

 

This is tedious and time-consuming. But, if NLP, NLU, and NLG work here. NLU and NLP can comprehend and decipher the text of the stock market, after which NLG will generate a story for publication on a website. As a result, it can function as a human while the user performs other tasks.

 

Natural language processing (NLP) is the process of converting unstructured language data into a structured data format so that machines can understand speech and text and formulate relevant, contextual responses. Natural language processing and natural language generation are among its subtopics.

 

Natural language understanding (NLU) is a branch of computer science that focuses on machine reading comprehension through grammar and context, allowing it to determine the intended meaning of a sentence.

 

Natural language generation (NLG) is the construction of text in English or other languages by a machine using a given dataset.