As a branch of Artificial Intelligence (AI) known as Natural Language Processing (NLP) uses computers to intelligently and usefully analyze, understand, and derive meaning from human language.
By utilizing NLU and NLP developers can carry out activities including named entity recognition, automatic summarization, translation, relationship extraction, sentiment analysis, speech recognition, topic segmentation, and sentiment analysis.
Later on, you will have a good idea about NLP vs NLU vs NLG .
What is Natural Language Processing?
NLP is the ability of AI products (including NLG vs NLU ) and services to processes large amounts of human language data and add context and derive meaning from human speech or written text, using statistical methods and machine learning algorithms.
Examples of NLP:
- Chatbot
- Text summarization
- Text categorization
- Parts of speech tagging
- Stemming
- Text mining
- Machine Translation
- Language modeling and others
For example, many languages don’t allow for straight translation and have different orders for sentence structure. With NLP, online translators can translate languages more accurately and present grammatically-correct results.
What is Natural Language Understanding?
NLU allows human languages to be understood statically by the computer. It solves it by understanding the context, semantic, syntax, intent, and sentiment of the text. For this purpose, various rules, techniques, and models are used. It finds the objective behind that text.
There are three levels to apply this process:
- Syntax: It understands sentences and phrases. It checks the grammar and syntax of the text.
- Semantic: It checks the meaning of the text.
- Pragmatic: It understands context to know what the text is trying to achieve.
What is Natural Language Generation ?
It is the use of AI to develop software that produces written or spoken text from a defined and arranged set of data so that it appears to have been generated by a human being. NLG is related to human-machine interaction, including computational linguistics, NLP and NLU .
NLG projects are formed using templates and conditions. Templates are essentially sentences with gaps that are filled using data source. Conditions are the circumstances that need to be met in order for the template to be utilized.
NLP vs NLU vs NLG ?
The following table includes the minor differences between NLP and NLU and NLG :
| NLP | NLU | NLG |
| Instead of treating any document as a collection of words, using the machine to understand the context of it | It investigates the methods by which computers can understand instructions given to it in human languages | It enables computers to produce output after comprehending user input in natural languages |
| Decision making | Discussing an understanding text | 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 realization of providing output in natural languages |
| NLP means how the machine processes the given data to make decisions, take actions, and respond to the system. | 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 | NLU vs NLG : 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, ecommerce reports, 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 | NLG apps 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 uses structured data to create the strategy for useful text generation. |
| It employs a learning mechanism to produce effective results | For understanding, it first converts natural language to machine language |
A quick look to the future of NLP
With evolution in the field of NLP technology machines will be able to understand humans more effectively and can derive understanding from the unstructured data available online.
Since one of the most important aspects of doing business is determining what the customer wants, many businesses are now spending money on researchers to develop better NLP algorithms that can perform sentiment analysis, yield important information from unstructured data, improve communication, and function more efficiently.
