NLU and NLP's need rose 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, purpose to understand the human language. But it is essential to understand the human language 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 are different.
The Turing Test: Computer and language are coming together from back 1950. With time they are trying to make more intelligent machines. It starts from simple language input to the training model and now going with complex language inputs. One famous example of language and Artificial Intelligence is "The Turing Test. " It was developed by Alan Turing in 1950 to check whether a machine is intelligent enough or not.
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 to read information, break it down, understand it, and making decisions to respond. Historically most common tasks of Natural Language Understanding are:
Let's take an example to understand it. In 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 take decisions according to various features such as whether it is raining or not? 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 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 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 is trying 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, dialogues agents, etc. Let's take an example for more clarity. If you asked: "How's today ?". Now, what if the system answers, "Today is October 1, 2020, and Thursday." Is the system providing you 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.
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:
There is a minor difference between both of them. That need to consider:
It is a narrow concept.
It is a wider concept.
It is a narrow 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 makes sure that it will infer correct intent and meaning even 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 make decisions, take actions, and respond to the system. It contains the whole End to End process. Every time it doesn't need to contain 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 use it 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, computer science, and more.
Here we will talk about everyday Artificial Intelligence problems to understand how they work together and change humans' whole experience while interacting with machines. If a user wants a simple chatbot, they can create it using it and Machine learning techniques. But if they're going to develop an intelligent contextual assistant, they need NLU. To create a human-like chatbot or natural sound conversational AI system, they are using NLP and NLU together. They are focusing on systems that can pass the Turing test. This system can quickly and effortlessly interact with people. This system can be possible by combining all linguistics and processing aspects.
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 form and makes analysis easy.
Stemming: It reduces inflected words by cutting words to their root form.
Morphological segmentation: It split 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.
The syntactic analysis does not always correlate sentence or text validation. Correct or incorrect grammar is not enough for that purpose. Other factors also need to be considered. Another thing is Semantic analysis. It is used to interpret the meaning of words. We have some techniques of semantic analysis:
Named entity recognition (NER): It identifies and classifies text into predefined groups.
Word sense disambiguation: It identifies the sense of words used in sentences. It gives meaning to a word based on the context.
Natural language generation: It converts structured data into language.
With semantics and syntactic analysis, there is one thing more that is very important. It is called Pragmatic analysis. It helps to understand the objective or what the text wants to achieve. Sentiment analysis helps to achieve this objective.
To prepare a human language AI system to pass the Turing test, developers focus on some essential terms. If we mathematically represent that whole End to End process, it contains the following terms: A combination of NLU and NLG gives an NLP system.
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.
To better understand their use take a practical example, you have a website where you have to post reports of the share market every day. For this task daily, you have to research and collect text, create reports, and post them on a website. This is boring and time-consuming. But, if NLP, NLU, and NLG work here. It and NLP can understand the share market's text and break it down, then NLG will generate a story to post on a website. Thus, it can work as a human and let the user work on other tasks.
NLP and NLU are significant terms to design the machine that can easily understand the human language, whether it contains some common flaws. There is a small difference between the terms, that are very important for the developers to know if they want to create a machine that can interact with humans by giving them a human-like environment because the use of the correct technique at the right place is essential to succeed in systems created for Natural Language operations.