NLP, NLU, and NLG Introduction
NLU and NLP’s needs have appreciated with advancements in technology and research, and computers can analyze and perform tasks for all sorts of data. But when we talk about human language, it changes the whole scenario because it is messy and ambiguous. It is complex to process human language rather than statistics. The system has to understand content, sentiment, and purpose to understand the human language. However, it is essential to understand the human tongue to know the customer’s intent for a successful business. Here, Natural Language Understanding and Natural Language Processing play a vital role in understanding human language. Sometimes, people use these terms interchangeably as they both deal with Natural Language. Their goal is to deal with the human language, yet they differ.
What is Natural Language Processing?
It is a subset of Artificial Intelligence. It processes large amounts of human language data. It is an end-to-end process between the system and humans. It contains the whole system, from understanding the information to making decisions while interacting. Such as reading information, breaking it down, understanding it, and making decisions to respond. Historically, the most common tasks of Natural Language Understanding are:
- Tokenization
- Parsing
- Information extraction
- Similarity
- Speech recognition
- Speech generations and others.
Real-life NLP Applications can be used for the:
- Chatbot
- Text summarization
- Text categorization
- Parts of speech tagging
- Stemming
- Text mining
- Machine Translation
- Ontology population
- Language modeling and others
Let’s take an example to understand it. In the chatbot, if a user asks: “Can I play volleyball?”. Then, NLP uses Machine Learning and AI algorithms to read the data, find keywords, make decisions, and respond. It will make decisions according to various features, such as whether it is raining. Is there any playground available or not? And Other accessories are available or not. Then, it responds to the user about playing or not. It contains the whole system, from taking input to providing output.
What is Natural Language Understanding?
It helps the machine to understand the data. It is used to interpret data to understand the meaning of data to be processed accordingly. It solves it by understanding the text’s context, semantics, syntax, intent, and sentiment. For this purpose, various rules, techniques, and models are used. It finds the objective behind that text. There are three linguistic levels to understand language.
- 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 aims to achieve.
It has to understand the unstructured text with flaws in the structured and correct format. It converts text into a machine-readable format. It is used for semantics phrasing, semantic analysis, dialogue agents, etc. Let’s take an example for more clarity. If you asked: “How’s today ?”. What if the system answers, “Today is October 1, 2020, and Thursday.” Is the system providing the correct answer? No, Because here, users want to know about the weather. Therefore, we use it to learn the text’s right meaning of some errors.
What is the Natural Language Generation?
NLG is a process to produce meaningful sentences in Natural Language. It explains the structured data in a manner that is easy to understand for humans with a high speed of thousands of pages per second. Some of the NLG models are listed below:
- Markov chain
- Recurrent neural network (RNN)
- Long short-term memory (LSTM)
- Transformer
What is the Difference Between NLP, NLU, and NLG?
There is a minor difference between both of them. What needs to be considered:
| NLU | NLP | NLG |
| It is a narrow concept. | It is a broader concept. | It is a limited concept. |
| If we only talk about an understanding text, then it is enough. | But if we want more than understanding, such as decision-making, then it comes into play. | It generates a human-like manner text based on the structured data. |
| It is a subset of NLP. | It is a combination of it and NLG for conversational Artificial Intelligence problems. | It is a subset of NLP. |
| It is not necessarily that what is written or said is meant to be the same. There can be flaws and mistakes. It ensures that it will infer correct intent and meaning even if data is spoken and written with some errors. It is the ability to understand the text. | But, if we talk about NLP, it is about how the machine processes the given data. Such as making decisions, taking action, and responding to the system. It contains the whole End-to-end process. Every time, it doesn’t need to have it. | It generates structured data, but it is not necessarily that the generated text is easy to understand for humans. Thus, NLG makes sure that it will be human-understandable. |
| It reads data and converts it to structured data. | It converts unstructured data to structured data. | NLG writes structured data. |
NLP and NLU Together
It is a subset of NLP. It can be used in NLP for a human-like understanding of data. It helps to achieve better it. It is the first step in many processes. It work together to give a human-like experience to the people. Processing and understanding language is not just about training a dataset. It is more than that. It contains several fields, such as data science, linguistic techniques, and computer science.Correlation Between NLP and NLU
There is a hypothesis driving it. It talks about the syntactic structure and states the aim of linguistic analysis. It is said to separate the grammatical sentences from non-grammatical sentences of language to check the sequence’s grammatical structure. Syntactic analysis can be used in various processes. There are multiple techniques to align and group words to check grammatical rules :- Lemmatization: It reduces the inflected forms of words by combining them into a single document and makes analysis easy.
- Stemming: It reduces inflected words by cutting words to their root form.
- Morphological segmentation: It splits words into morphemes.
- Word segmentation: It divides a continuous written text into distinct meaningful units.
- Parsing: It analyses words or sentences by underlying grammar.
- Part-of-speech tagging: This analyses and identifies parts of speech for each word.
- Sentence breaking: It detects and places sentence boundaries in continuous text.
- Named entity recognition (NER) identifies and classifies text into predefined groups.
- Word sense disambiguation: It identifies the sense of words used in sentences. It gives meaning to a talk based on the context.
- Natural language generation: It converts structured data into language.
- NLP (Natural Language Processing): It understands the text’s meaning.
- NLU (Natural Language Understanding): Whole processes such as decisions and actions are taken by it.
- NLG (Natural Language Generation): It generates the human language text from structured data generated by the system to respond.
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