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introduce
Natural language processing is one of the hottest topics in data science. The company has invested a lot of money in research in this area. Everyone is trying to understand natural language processing and its applications for a living.
Do you know why?
Because in just a few short years, natural language processing has grown into something more powerful and influential than anyone could have imagined.
To understand the power of natural language processing and its impact on our lives, we need to look at its application. Therefore, I have listed the top ten applications of natural language processing.
So, let’s start with the first application of natural language processing.
Search autocorrect and autocomplete
Whenever you search for something on Google, after 2-3 letters, it shows possible search terms. Or, if you search for something with typos, it will correct them and still find relevant results that work for you. Isn’t that amazing?
It’s something that everyone uses every day, but never pays much attention to. This is a good application of natural language processing and a good example. It affects millions of people around the world, including you and me.
Search autocomplete and autocorrect both help us find accurate results more efficiently. Now, many other companies are starting to use this feature on their sites, such as Facebook and Quora.
The driving engine behind search autocomplete and autocorrect is the language model.
Language translation
Have you ever used Google Translate to find a word or phrase in different languages? It’s amazing how easy it is to translate a text from one language into another, right? The technology behind it is machine translation.
Machine translation is the process of automatically converting text in one language into another while keeping the original meaning unchanged.
In the early days, machine translation systems were dictionary-based and rules-based systems with very limited success rates.
However, thanks to advances in the field of neural networks, the availability of vast amounts of data and powerful machines, machine translation has become quite accurate in converting text from one language to another.
Today, tools like Google Translate make it easy to convert text from one language to another. These tools are helping many people and businesses break the language barrier and succeed.
Social media monitoring
Today, more and more people are using social media to post their views on a particular product, policy or issue. This information may contain useful information about a person’s likes and dislikes.
Therefore, analyzing these unstructured data can help generate valuable information. Natural language processing also comes into play here.
Today, companies use a variety of NLP technologies to analyze social media posts to see what customers think about their products. The company is also using social media monitoring to understand the problems customers are facing with their products.
Not just companies, but even governments use it to identify potential threats related to national security.
Chatbot
Customer service and experience are the most important things for any company. It can help companies improve their products and satisfy customers. But manually interacting with each customer and resolving problems can be a tedious task.
Chatbots can help solve this situation by helping companies achieve their goal of a smooth customer experience.
Today, many companies use chatbots on their apps and websites, which can solve customers’ basic queries. Not only does it make the company’s process easier, but it also relieves customers from the frustration of waiting to call customer service for help.
In addition, it can reduce the cost of hiring customer service for the company. Chatbots started out as tools to solve customer queries, but today they have evolved into personal companions. Chatbots can do everything from recommending products to getting customer feedback.
Investigation and analysis
Research is an important way to evaluate a company’s performance. The company conducts many surveys to get customer feedback on various products. This is very useful for understanding defects and helping companies improve their products.
However, problems arise when a large number of customers are surveyed and the amount of data increases. One cannot read them all and draw conclusions. This is where companies use natural language processing to analyze surveys and mine them for information.
For example, learn how users feel about events from feedback and analyze product reviews to understand the pros and cons. Today, most companies use these methods because they provide more accurate and useful information.
Targeted advertising
One day, I was searching for a phone on Amazon, and a few minutes later, Google started showing me mobile-like ads on various web pages. I’m sure you’ve already been there.
Do you know what’s going on here? Targeted advertising!
Yeah! You read targeted ads correctly. Targeted advertising is a type of online advertising that shows users ads based on their online activity.
Now most online companies use this method, because first, it saves the company a lot of money; Second, relevant advertising is only shown to potential customers.
The work of targeted advertising is mainly keyword matching. Ads are associated with keywords or phrases and are only shown to users who search for keywords similar to the keywords associated with the AD.
Obviously, that’s not enough, and other factors, such as the websites they’ve recently visited, and the pages they’re interested in, are taken into account to provide users with relevant ads for products they might be interested in.
Recruitment and Job Search
The HUMAN resources department is an integral part of every company. Their most important job is to choose the right people for the company.
But today, in this competitive world, recruiters need to review hundreds of resumes for a single position. Sifting through resumes and screening candidates can take hours. Can this task be automated?
Right! With the help of natural language processing, recruiters can easily find suitable candidates. That means recruiters don’t have to go through every resume and manually screen out the right candidates.
Similar to information extraction for named entity recognition, this technology can be used to extract information such as skills, name, location and education. These characteristics are then used to represent candidates in the feature space and classify them into categories suitable or unsuitable for a particular role. Or, they can recommend a different role based on the resume.
This allows for an unbiased screening of resumes and the selection of the best candidate for an open position without requiring too much manpower. Most companies use application tracking systems to effectively screen resumes.
Voice assistant
I’m sure you’ve already met them, Google Assistant, Apple Siri, Amazon Alexa. Yes, these are all voice assistants.
A voice assistant is software that uses speech recognition, natural language understanding and natural language processing to understand a user’s spoken commands and perform actions accordingly.
You could say it’s similar to chatbots, but I’ve included voice assistants in their own right because they deserve a better place on this list. They’re not just chatbots, but they can do a lot more than chatbots.
Today, most of us can’t imagine life without a voice assistant. Over the years, they have become a very reliable and strong friend. Voice assistants can do everything from setting our alarm clocks to finding us a restaurant. They open new doors of opportunity for users and companies.
Grammar checker
This is one of the most widely used applications in natural language processing. Grammar checkers like Grammarly offer a ton of features that can help people write better content. They can turn any ordinary text into beautiful literature.
If you want to write an email to your boss, or if you want to write a report or better yet an article, there’s no denying that you need these helpful friends.
These tools can correct grammar, spelling, suggest better synonyms, and help deliver content with greater clarity and engagement.
They also help with readability, allowing you to convey your message in the best possible way. If you look at the grammar-checking tools of five years ago, they are far less capable than they are today.
Do you know why?
Natural Language processing came out in 2017.
Email filtering
Have you ever used Gmail?
I’m sure you’ve noticed that whenever you get it, it’s social. Best of all, spam is also filtered into a separate section. Is it both magical and beneficial? Yes, that’s what mail filtering is all about. I don’t have to tell you how much our daily work depends on this feature.
Filter E-mail using text categorization, a natural language processing technique. You might have guessed it.
Text classification is the process of classifying a piece of text into a predefined category. Another good example of text categorization is the categorization of news articles into different categories.
At the end
Now that you’re familiar with natural language processing applications, you’re ready to dive into the natural language processing world.
The original link: www.analyticsvidhya.com/blog/2020/0…
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