Natural Language Processing is among the hottest topic in the field of data science. Companies are putting tons of money into research in this field. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow.
Do you know why?
Because just in a few years’ time span, natural language processing has evolved into something so powerful and impactful, which no one could have imagined. To understand the power of natural language processing and its impact on our lives, we need to take a look at its applications. Therefore, I have put together a list of the top 10 applications of natural language processing.
So, let’s start with the first application of natural language processing.
Search autocorrect and autocomplete
Whenever you search something on Google, after typing 2-3 letters, it shows you the possible search terms. Or, if you search for something with typos, it corrects them and still finds relevant results for you. Isn’t it amazing?
It is something that everyone uses daily but never pays much attention to it. It’s a wonderful application of natural language processing and a great example of how it is affecting millions around the world, including you and me. Search autocomplete and autocorrect both help us in finding accurate results much efficiently. Now, various other companies have also started using this feature on their websites, like Facebook and Quora.
The driving engine behind search-autocomplete and autocorrect are the language models. You can read more about language models in this article: A Comprehensive Guide to Build your own Language Model in Python!
Have you ever used Google Translate to find out what a particular word or phrase is in a different language? I’m sure it’s a YES!! and the ease with which it translates a piece of text in one language to another is pretty amazing, right? The technique behind it is Machine Translation.
Machine Translation is the procedure of automatically converting the text in one language to another language while keeping the meaning intact.
In earlier days, machine translation systems were dictionary-based and rule-based systems, and they saw very limited success. However, due to evolution in the field of neural networks, availability of humongous data, and powerful machines, machine translation has become fairly accurate in converting the text from one language to another.
Today, tools like Google Translate can easily convert text from one language to another language. These tools are helping numerous people and businesses in breaking the language barrier and becoming successful. Do you want to know about the technique used in Google Translate? Then here is a must-read article for you.
Social media monitoring
More and more people these days have started using social media for posting their thoughts about a particular product, policy, or matter. These could contain some useful information about an individual’s likes and dislikes. Hence analyzing this unstructured data can help in generating valuable insights. Natural Language Processing comes to rescue here too.
Today, various NLP techniques are used by companies to analyze social media posts and know what customers think about their products. Companies are also using social media monitoring to understand the issues and problems that their customers are facing by using their products. Not just companies, even the government uses it to identify potential threats related to the security of the nation.
If you are also excited about leveraging the natural language processing for monitoring social media, then here are few articles to start your journey:
- Comprehensive Hands-on Guide to Twitter Sentiment Analysis with dataset and code
- Measuring Audience Sentiments about Movies using Twitter and Text Analytics
- Sentiment Analysis of Twitter Posts on Chennai Floods using Python
Customer service and experience are the most important thing for any company. It can help the companies improve their products, and also keep the customers satisfied. But interacting with every customer manually, and resolving the problems can be a tedious task. This is where Chatbots come into the picture. Chatbots help the companies in achieving the goal of smooth customer experience.
Today, many companies use chatbots for their apps and websites, which solves basic queries of a customer. It not only makes the process easier for the companies but also saves customers from the frustration of waiting to interact with customer call assistance.
Additionally, it can reduce the cost of hiring call center representatives for the company. Initially, chatbots were only used as a tool that solved customers’ queries, but today they have evolved into a personal companion. From recommending a product to getting feedback from the customers, chatbots can do everything.
Surveys are an important way of evaluating a company’s performance. Companies conduct many surveys to get customer’s feedback on various products. This can be very useful in understanding the flaws and help companies improve their products.
But, the problem arises when a lot of customers take the survey leading to increasing data size. It becomes impossible for a person to read them all and draw a conclusion. That’s where companies use natural language processing to analyze the surveys and generate insights from them, like knowing the sentiments of users about an event from the feedbacks and analyzing product reviews to understand the pros and cons. Today, most of the companies use these methods because they provide much more accurate and useful information.
Here’s how you can make a chatbot all by yourself:
One day I was searching for a mobile phone on Amazon, and a few minutes later, Google started showing me ads related to similar mobile phones on various webpages. I am sure you have experienced it.
Do you know what happened here?
Yeah! You read it right targeted advertising. Targeted advertising is a type of online advertising where ads are shown to the user based on their online activity. Most of the online companies today use this approach because first, it saves companies a lot of money, and second, relevant ads are shown only to the potential customers.
Targeted advertising works mainly on Keyword Matching. The Ads are associated with a keyword or phrase, and it is shown to only those users who search for the keyword similar to the keyword with which the advertisement was associated. Obviously, that’s not enough, there are other factors like the recent websites they visited, and the webpages they showed interest in, are all taken into account to provide the users with the relevant advertisements of products that they might be interested in. For reading more about the Keyword Matching, click here.
Hiring and recruitment
The Human Resource department is an integral part of every company. They have the most important job of selecting the right employees for a company. But, today, in this highly competitive world, recruiters need to review hundreds or sometimes thousands of resumes for a single position. It might take hours for filtering resumes and shortlisting the candidates. Can this task be automated?
Yes! With the help of natural language processing, recruiters can find the right candidate with much ease. This simply means that the recruiter would not have to go through every resume and filter the right candidates manually. The technique, like information extraction with named entity recognition, can be used to extract information such as skills, name, location, and education. Then, these features can be used to represent the candidates in the feature space, and then they can be classified into the categories of fit or not-fit for a particular role. Or, they can also be recommended a different role based on their resume.
This allows the unbiased filtering of resumes and selection of the best possible candidates for a vacant position without requiring much human labor. Most of the companies use Application Tracking Systems for screening the resumes efficiently.
I am sure you’ve already met them, Google Assistant, Apple Siri, Amazon Alexa, ring a bell? Yes, all of these are voice assistants.
A voice assistant is a software that uses speech recognition, natural language understanding, and natural language processing to understand the verbal commands of a user and perform actions accordingly. You might say it is similar to a chatbot, but I have included voice assistants separately because they deserve a better place on this list. They are much more than a chatbot and can do many more things than a chatbot can do.
Today, most of us cannot imagine our lives without voice assistants. Throughout the years, they have transformed into a very reliable and powerful friend. From setting our morning alarm to finding a restaurant for us, a voice assistant can do anything. They have opened a new door of opportunities for both users and companies.
This is one of the most widely used applications of natural language processing. Grammar Checking tools like Grammarly provides tons of features that help a person in writing better content. They can change any ordinary piece of text into beautiful literature. If you want to write an email to your boss or if you’re going to write a report or better an article, there is no denying the fact that you need these helpful friends.
These tools can correct grammar, spellings, suggest better synonyms, and help in delivering content with better clarity and engagement. They also help in improving the readability of content and hence allowing you to convey your message in the best possible way. If you take a look at the condition of grammar checkers five years back, you’ll find that they weren’t nearly as capable as they are today.
You know why?
Because transformers arrived in 2017.
I am talking about the transformers which are used in natural language processing. am talking about the transformers which are used in natural language processing. They sound interesting, aren’t they? Then you should read this article, which explains everything about the transformers: How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Models
Have you ever used Gmail?
I’m sure you have, then you might have already noticed that whenever a mail arrives, it gets classified into the sections of primary, social, and promotions. And the best thing is that the spam emails are also filtered out to a separate section. Isn’t it amazing and beneficial at the same time? Yes, it is, and that’s all email filtering is. And I don’t have to tell you how much our daily tasks rely on this feature.
The emails are filtered using text classification, which is a natural language processing technique. And as you might have already guessed it. Text Classification is the process of classification of a piece of text into pre-defined categories. Another great example of text classification is the classification of news articles into various categories. Here are few resources to getting you started with text classification:
- A Comprehensive Guide to Understand and Implement Text Classification in Python
- Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
- Build Your First Text Classification model using PyTorch