NLP vs. NLU- Understanding the Distinction
Think of human communication. When we hear or read something our brain first processes that information and then we understand it. Based on our understanding, we respond. We don’t understand everything that is told to us. That is because we can’t process all information – we can only process information that is within our familiar realm.
The machine learning process is similar to that for humans: First, a program must be able to interpret information, then create knowledge from it. Natural Language Processing (NLP) refers specifically the ability for machines to gather and make sense of language; Natural Language Understanding (NLU) relates more closely with understanding human speech or text from the processed information.
What is Natural Language Processing (NLP)
Natural language processing is a field of computer science that works with human languages. It aims to make machines capable of understanding human speech and writing and performing tasks like translation, summarization, etc. NLP has applications in many fields, including information retrieval, machine translation, chatbots, and voice recognition.
How Does Natural Language Processing (NLP) Work
Natural language processing (NLP) involves using algorithms to analyze human languages. The NLP process is based on machine learning and natural language understanding. NLP consists of a set of tasks that can be divided into certain categories:
Lexical Analysis
This involves the process of identifying words and their meanings.
- Tokenization: The task of dividing a sentence into individual words is called tokenization.
- Part-of-Speech Tagging (PoS): PoS tagging involves assigning parts of speech to each word in a sentence based on its context.
Syntactic Analysis
The syntactic analysis involves the process of identifying the grammatical structure of a sentence. It also consists in detecting errors in grammatically incorrect sentences.
Semantic Analysis
The semantic analysis involves the process of assigning the correct meaning to each word in a sentence. A semantic parser is designed for this purpose.
Output Transformation
The output transformation is the final step in NLP and involves transforming the processed sentences into a format that machines can easily understand. For example, if we want to use the model for medical purposes, we need to transform it into a format that can be read by computers and interpreted as medical advice.
Best Use Cases of Natural Language Processing (NLP)
Natural language processing is used in many applications and can be used as stand-alone software or as a part of another product. Below are some of the best use cases:
Checking Grammatical Errors
It is common for people to make grammatical errors while writing. NLP can be used to detect these errors and correct them automatically. This will help improve the readability of content by reducing the number of grammatical errors.
Translation
Natural language processing is used in the translation of content. It can be used to translate text from one language to another and even generate automatic translations of documents. This allows users to read content in their native language without relying on human translators.
Chatbot Integration
A chatbot is a combination of NLU and Dialog Management. Chatbots are used by businesses to interact efficiently with their customers. NLP can be used to integrate chatbots into websites, allowing users to interact with the business directly through their website. This will help improve customer satisfaction and save company costs by reducing the need for human employees who would otherwise be required to provide these services.
Sentence Completion
Sentence completion is a widespread use of NLP and can be used to generate text automatically. This is done using the context of sentences that have already been written, allowing computers to predict the next word in a sentence. This could be used to write poetry or generate song lyrics, but it can also be used for more practical applications such as writing emails or letters with little effort from users.
Insight Extraction from Data Analytics
Data Analytics is a field of NLP that uses machine learning to extract insights from large data sets. This can be used to identify trends and patterns in data, which could be helpful for businesses looking to make predictions about their future.
What is Natural Language Understanding (NLU)
Natural Language Understanding (NLU) is a field of NLP that allows computers to understand human language in more than just a grammatical sense. It also means they can comprehend what the speaker or writer is trying to say and its intent. Businesses could use this for customer service applications such as chatbots and virtual assistants.
How Does Natural Language Understanding (NLU) Work
Natural language understanding uses various techniques and algorithms to process the data it receives. For a computer to truly understand human language, it needs to be able to do the following things:
Understanding & Determining an NLU Intent
The first step in natural language understanding is to determine the intent of what the user is saying. Upon successful determination of this, it can be used to filter out any irrelevant data for further processing. For example, if a user asks their phone, “What’s the weather like today?” they probably don’t want to know about tomorrow or last week. Instead, they want an answer as quickly as possible to make plans accordingly.
Identifying NLU Entities
Once an intent has been determined, the next step is identifying the sentences’ entities. These are usually nouns that denote a person, place, or thing. For example, if someone says, “I went to school today,” then the entity would likely be “school” since it’s the only thing that could have gone anywhere.
How Does NLU Train Data
There are many different ways to train data, each with its pros and cons. The most common way is to use a supervised learning algorithm, like linear regression or support vector machines. These algorithms work by taking in examples of correct answers and using them to predict what’s accurate on new examples.
The Significance of Natural Language Understanding (NLU) Training Data
The importance of NLU data with respect to NLU has been widely recognized in recent times. The significance of NLU data with respect to NLU is that it will help the user to gain a better understanding of the user’s intent behind the interaction with the bot.
It also helps in eliminating any ambiguity or confusion from the conversation. The more data you have, the better your model will be able to predict what a user might say next based on what they’ve said before.
NLP & NLU- Do We Need Both
NLP and NLU are both essential to a sound AI system. They’re closely related and sometimes confused for one another. The main difference between them is that NLP deals with language structure, while NLU deals with the meaning of language.
This is why they’re both critical. If you only have NLP, then you can’t interpret the meaning of a sentence or phrase. Without NLU, your system won’t be able to respond appropriately in natural language.
Frequently Asked Questions
Are NLP and NLU the same?
No, they’re not the same. NLP deals with language structure, and NLU deals with the meaning of language.
How does natural language understanding work in AI?
NLU enables human-computer interaction by comprehending commands in natural languages, such as English and Spanish.
What’s the importance of NLP as an applied area of AI?
NLP allows us to resolve ambiguities in language more quickly and adds structure to the collected data, which are then used by other systems.