Top 10 Applications for Natural Language Processing (NLP)

Understanding NLP

 

Natural Language is all about how humans communicate with each other, figuratively through text and speech. As you are reading this article, you are being communicated through text. In this modern era of technology where computers are a quintessential part of our lives, it is very important for us to communicate with our computers. 

 

Over the years, Artificial Intelligence has overdone it’s ability to make things happen in the world of technology. Natural Language Processing (NLP) is a component of AI in the field of linguistics that deals with interpretation and manipulation of human speech or text using software. It enables the computer to understand the natural way of human communication by combining machine learning, deep learning and statistical models. 

 

Due to the vast availability of Big Data, Modified Algorithms and Powerful devices, NLP is a rapidly advancing technology. There are several ways to approach NLP, starting from statistical and machine learning to rule based and algorithmic approaches. 

 

There are several tools provided to work in NLP. Python programming language provides a Natural Language Toolkit (NLTK) and other open source libraries and educational resources for NLP programming. Statistical Analysis combines Machine Learning and Deep Learning models along with Computer Algorithms to extract and differentiate the text and voice data and statistically provide meaning to all the elements. 

 

Currently, Neural NLP is progressing since Representation Learning and Deep Neural Network-style machine learning started widespread in NLP.  NLP helps in a vast range of services starting from Business Analytics, Speech recognition, Social media etc. 

 

Let us discuss the Top 10 Applications of Natural Language Processing.

 

 

NLP Applications

 

1. Speech Recognition

 

Have you ever wondered while talking to your Google Assistant or any other voice assistant about how they recognise your voice and interpret the meaning of your words and give you an appropriate response? This is because of Speech Recognition.

 

Speech Recognition is a technology that enables the computer to convert voice input data to machine readable format. There are a lot of fields where speech recognition is used like, virtual assistants, adding speech-to-text, translating speech, sending emails etc. 

It is used in search engines where the user can voice out the name of their search requirements and get the desired result, making our work easier than typing out the entire command.

 

(Must read: Machine learning algorithms)

 

 

2. Voice Assistants and Chatbots

 

All of us are well versed with the idea of Voice assistants like Alexa, Siri and Google Assistant, and chatbots that are integrated in many websites to help and guide new users. 

 

Voice assistant is a software that uses NLP and speech recognition to understand voice commands of a user and perform accordingly. Similarly, Chatbots are programs that are designed to assist an user 24/7 and respond appropriately and answer any query that the user might have.

 

Most Chatbots and Virtual Assistants have pre-programmed answering systems that follow specific rules and patterns while answering. Powerful AI has enabled some voice assistants to interact with the user and respond appropriately. With more usage, they even improve themselves. Assistants like Siri and Alexa can even have a conversation with the user like a normal human being!
 

 

3. Auto Correct and Auto prediction

 

There are many softwares available nowadays that check grammar and spelling of the text we type and save us from embarrassing spelling and grammatical mistakes in our emails, texts or other documents. NLP plays an important role in those softwares and functions.

 

This is one of the most widely used applications of NLP. These softwares offer a lot of features like suggesting synonyms, correcting grammar and spellings, rephrasing sentences and giving clarity to the document and can even predict the tone of the sentence that might be implied by the user. 

 

Auto prediction is also a feature developed through NLP where the computer suggests automatic prediction of the text we have started typing. This saves time of the user and makes the job easier for them

 

(Also read: Examples of NLP)
 

 

4. Email Filtering

 

Most of the professional work is done through emails and it would be quite a hassle if all the emails we received were not segregated into different sections. Gmail classifies all the emails into primary, social and promotional sections. Even all the spam emails are sent to a different section so that they do not flood our inbox. 

 

This is done with the help of text classification, which is a technique of NLP.  It has definitely helped us save time and not miss any important Email that might have gotten lost if all the useless emails started accumulating in our inbox.

 

 

5. Sentiment Analysis

 

Human speech could be quite hard to interpret as it involves expressions and sentiments beyond literal meanings. Expressions like sarcasm, threat, exclamation etc. are often very hard to be recognised by the computer.

 

But, with the help of Natural Language Understanding (NLU) which is a subfield of Natural Language Processing, the machine is able to catch on to different sentiments that might be insisted on through the user’s command.  

 

Through sentiment analysis, one can analyse customer reactions, handle social media disputes by eradicating negative comments and getting insights from the customer base of any business.

 

 

6. Advertisement to Targeted Audience

 

If you ever search any product or object in any shopping site, you would often see ads of those products and other related products on other sites. This type of targeted online advertising is done with the help of NLP and it is known as Targeted Advertising. 

 

Through NLP, keywords that are searched by the user are matched with the keywords of the product ad. If they are similar, the user gets an advertisement. This process is called keyword matching.

 

This has been highly beneficial to many companies and saved them a lot of investment as the ads are only shown to customers who are actually interested in the product, which is determined on the basis of their online activity. 

 

 

7. Translation

 

Social Media has brought the entire world together but with unity comes challenges like language barrier. With different translating softwares that work individually or are integrated within other applications, this hurdle has been easily defeated.

 

This is called Machine Translation, which uses Natural Language Processing,  and has made a lot of improvement in the field due to availability of huge amounts of data and powerful machines, and advancement in the field of Machine learning and Neural networking.

 

It has particularly helped businesses in grabbing foreign customers and social media users who are connected to people overseas. Platforms like Facebook and Instagram have their own translation software integrated within the main application.

 

 

8. Social Media Analytics

 

Social Media is an integral part of everyone’s life nowadays and many people use it to post their thoughts about different businesses and products. 

 

The companies can understand their market position and get their customer reviews by analyzing the data.  But due to the huge number of users, the information could be very hard to grasp. It is not convenient to go through millions of comments and updates just to get the insights.

 

This is where Natural Language Processing and Sentiment analysis is used to identify and structure the data provided by the users and give back the relevant feedback and other necessary insights to the business.

 

 

9. Recruitment 

 

In this competitive world, big and small companies are on the receiving end of thousands of resumes from different candidates. It has become a tough job for the HR team to go through all the resumes and select the best candidate for one single position.

 

NLP has made the job easier by filtering through all the resumes and shortlisting the candidates by different techniques like information extraction and name entity recognition. It goes through different attributes like Location, skills, education etc. and selects candidates who meet the requirements of the company closely.

 

This is one of the most practical and unbiased ways to filter resumes without human interference or labour. It also recommends other fitting positions for the candidate if there are any vacancies.

 

(Also check: Top 10 NLP trends)

 

 

10.  Text Summarisation

 

There is a huge amount of data available on the internet and it is very hard to go through all the data to extract a single piece of information. With the help of NLP, text summarization has been made available to the users. This helps in the simplification of huge amounts of data in articles, news, research papers etc. 

 

This application is used in Investigative Discovery to identify patterns in writing reports,  Social Media Analytics to track awareness and identify influencers, and Subject-matter expertise to classify content into meaningful topics.

 

Conclusion

 

With the advancement in technology, several subfields of NLP are emerging, including Natural Language Understanding (NLU), Semantic analysis etc. that helps to understand the intended meaning of a speech or text through complex algorithms. Since NLP is used in many fields today and has efficiently made communication with devices easier, it is one of the most popular fields to study in Artificial Intelligence.