NLU and NLP's needs rose with advancements in technology and research. Computers can analyze and perform tasks for all sorts of data. But human language changes the whole scenario because it is messy and ambiguous. It is more complex to process human language than statistics.
The amount of analyzed data 'touched' by cognitive systems will grow by a factor of 100 to 1.4 ZB by 2025 Source- The Evolution Of NLP And Its Impact On AI
The Turing Test: Computer and language have been coming together since 1950. With time, they are trying to make more intelligent machines. It starts with simple language input, goes to the training model, and then goes on to 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.
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:
It helps the machine understand the data. It is used to interpret data to understand its meaning so that it can be processed accordingly. It solves the problem 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 the text. There are three linguistic levels to understanding 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 correct meaning of some errors.
It is a subset technique of Artificial Intelligence that is used to narrow the communication gap between the Computer and Human. Click to explore about, Evolution and Future of Natural Language Processing
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 the generated text is not necessarily 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. |
Here, we will discuss everyday Artificial Intelligence problems to understand how they work together and change humans' 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 develop an intelligent contextual assistant, they need NLU.
To create a human-like chatbot or natural sound conversational AI system, they use NLP and NLU together. They are focusing on strategies 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.
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.
The syntactic analysis does not always correlate with 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) 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.
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 better understand their use, take a practical example: you have a website where you have to post reports of the share market daily. For this daily task, you must 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, and 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
Conclusion of NLP vs. NLU vs. NLG
NLP and NLU are significant terms for designing a machine that can easily understand human language, regardless of whether it contains some common flaws. There is a slight 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.
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