What is Natural Language Processing (NLP) - Explained in Deep

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Natural language processing, or NLP, is the brain of computers that helps them understand and talk like humans. It's a part of Artificial Intelligence. Additionally, it makes personal assistants so clever in different jobs. NLP takes your words, figures out what you mean, and does things accordingly. Moreover, it is in high demand because it's very effective, and everyone wants it in today's market. Before proceeding further, we will elaborate on the meaning of NLP in detail.

NLP Meaning

Natural Language Processing (NLP) is a field of artificial intelligence and computer science. It teaches machines to understand how we humans talk. NLP uses computational linguistics and models based on numbers and learning tricks. So, when you talk to your computer or phone, NLP helps it figure out what you mean. 

NLP powers many applications that utilize languages, such as voice recognition, text translation, chatbots, and text summarization. Apart from that, you’ve already used some applications yourself, such as digital assistants, voice-operated GPS systems, customer service bots, and speech-to-text software. Moreover, it also helps businesses improve their productivity, efficiency, and performance by facilitating complicated tasks that involve language.

Why is Natural Language Processing Necessary?

Natural Language Processing (NLP) is a tool that helps computers understand what we say and write, even if we talk in different ways or use slang. It's like a language wizard for machines.

Businesses use NLP to do things automatically, like:
 

• Reading and organizing bulky documents.
• Listening to what customers say on calls and giving feedback.
• Having chatbots talk to customers and answer simple questions.
• Helping with questions like who, what, when, and where.
• Sorting and pulling out important information from text.

Natural Language Processing is also great for making apps that talk nicely to customers. In addition, it works as a chatbot that speaks to you and helps with common questions. This way, companies save money, and people working in customer service have more time for important stuff. Plus, customers are happier when things are quick and easy. 

How NLP Works?

As we know, NLP is like giving computers the power to understand how we talk, whether we're saying things out loud or writing them down. Moreover, computers have special tools like programs and microphones to listen and collect sounds. So, when you talk or type something, NLP works and turns it into a language the computer can understand.

Sometimes, you have a bunch of text and want a computer to understand it. First, get the text ready. This step is called 'data preprocessing.
 

• Tokenization: It replaces secret stuff in your text with codes. As a result, it keeps essential things safe.
• Stop Word Removal: We remove common words that don't tell us much, so we focus on the important ones. 
• Lemmatization and Stemming: This is like finding the main idea of a word. For example, changing 'running' to 'run' so the computer understands better.
• Part-of-speech Tagging: Words get tags like 'noun,' 'verb,' or 'adjective,' so the computer knows their job in the text.

Once our text is ready, we teach the computer how to understand it. There are two main ways:
 

• Rule-based System: It includes a set of rules to follow. 
• Machine Learning-based System: This is like teaching a computer to learn from examples. It uses lots of data to figure out how things work. 

Natural Language Processing Techniques

It's not easy because human language is tricky, with lots of funny things like words sounding the same, jokes, and different ways to say things.

Now, there are tasks to help the computer figure out what we mean:
 

• Speech Recognition (Speech-to-Text): Turning what we say into written words.
• Part of Speech Tagging: It says if a word is doing a job as a verb (like 'run') or a noun (like 'dog'). Like figuring out if 'make' means 'create' or 'choose' in a sentence.
• Word Sense Disambiguation: It helps the computer understand which meaning of a word is correct in a sentence. Like knowing if 'make' means 'achieve' or 'place.'
• Name Entity Recognition (NEM): Spotting important words like names or places. Like finding 'Kentucky' as a location or 'Fred' as someone's name is the perfect example of NLP.
• Co-reference Resolution: Figuring out when words in a sentence refer to the same thing. Like knowing 'she' means 'Mary.'
• Sentiment Analysis: Catching feelings and emotions from what we write. Is it happy, sad, or maybe a joke?
• Natural Language Generation: It's turning its information into words we can understand. In short, an example is the opposite of turning our words into computer language.

So, these tasks help the computer and make sense of our words. Now, we will learn some applications. 


Learners Also Read:- What is Neural Network - Definition | Types | Tools

Applications of Natural Language Processing

The following section will elaborate on some applications and uses of NLP.
 

• Automating Boring Tasks: NLP helps chatbots do various everyday tasks, like finding information in large databases and answering questions. In short, people can focus on more jobs.
• Making Search Better: It helps improve online searches by understanding words in multiple ways. Like if you're looking for a "car," it knows you might mean "automobile" and not something else.
• Getting Found Online: It boosts your business in online searches. It's using secret codes to tell search engines your business is the best so more people see it.
• Sorting Big Documents: NLP organizes big piles of documents, like reports or news articles.
• Checking Social Media: NLP reads numerous comments on social media to see if people are happy or not. In addition, it helps businesses know what customers like or don't like, making them even better.
• Understanding Customers: It listens to customers' words, helping businesses know what customers want.
• Keeping Things Nice: NLP helps businesses read multiple comments and keeps things polite and good.

Now, we will discuss the NLP projects that are handled by famous companies. 

Top NLP Companies

NLP is a helpful tool for businesses, especially when they have multiple words to deal with, like emails or social media chats. Here's how it works in different jobs:
 

• Healthcare: In hospitals, NLP helps doctors understand electronic records faster. It's a quick helper that looks at lots of health information to find important things.
• Legal Assistant: Instead of spending hours reading papers, it quickly finds important stuff for cases, saving time and stopping mistakes.
• Money-Wizards: In the money world, traders use NLP to find beneficial info from news and documents.
• Customer Service Helpers: Big companies use NLP to teach computers to talk to customers. It helps answer effortless questions, like where to find info, so people get quick help.
• Insurance: In big insurance offices, NLP faces loads of documents to find details about claims.

Further, we’ll elaborate on popular NLP models that require some tools. 

Natural Language Processing Tools

Find below a list of NLP tools and platforms that can be used to analyze news content, get insights from text, and perform advanced NLP tasks. 
 

• Aylien: An NLP platform that leverages news content to extract insights and provide analysis.
• MonkeyLearn: A simple and user-friendly NLP tool that can be used for text analysis and classification.
• Google Cloud NLP API: A Google technology-based NLP platform that can be used to perform a wide range of NLP tasks.
• IBM Watson: A pioneering AI platform for businesses that can operate to perform various NLP tasks, including language translation, sentiment analysis, and more.
• NLTK: The most popular Python library for NLP use for a wide range of NLP tasks.
• Amazon Comprehend: An NLP service offered by Amazon Web Services (AWS) that can be used to get insights from text.
• Stanford Core NLP: A powerful and fast toolkit developed by Stanford University for performing NLP tasks.
• SpaCy: A super-fast and efficient Python library for advanced NLP tasks, including named entity recognition, dependency parsing, and more.
• TextBlob: An easy-to-use Python library that provides an intuitive interface for NLTK. 
• GenSim: A state-of-the-art platform for performing topic modeling and other NLP tasks.

Conclusion

The above-mentioned guide is your friendly map for diving into Natural Language Processing (NLP). It's a roadmap that helps you understand the basics and find helpful tools to explore the in-depth concept of NLP. Whether you're just starting or already know a bit, this guide is here to make you feel confident.

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