Natural Language Processing is definitely something you might have come across!

You might be unclear as to what it exactly means but we have you covered there. Today we will tackle the concept and also the applications of Natural Language Processing to help you understand what it actually means and how are different businesses profiting off NLP.

What is Natural Language Processing?

Natural Language Processing is a part of Artificial Intelligence. 

Computer programs usually require human instructions fed to them in a language they can understand to deliver desired outcomes.

Natural Language Processing is the ability of such programs to understand and interpret instructions as fed to them in human language.

With Natural Language Processing, computer programs can understand different people in their language of choice like Google Assistant. Google Assistant is, in fact, one of the most famous applications of Natural Language Processing.

Humans use either text or speech in all their interactions. NLP finds utility in both kinds of expression. Just like Google Assistant helps with spoken instructions, a number of other applications of Natural Language Processing include help with the written word.

Ever written an e-mail which was automatically corrected or completed? Well, that’s Natural Language Processing for you! One such popular application is Grammarly.

Now that you have an overview of Natural Language Processing, let’s see how and where it really works!

How does Natural Language Processing work?

Natural Language Processing is the ability of the computer to understand text and speech in human language itself. 

NLP is a combination of Artificial Intelligence, Computational liguistics and Computer Science. 

Earlier, Natural Language Processing techniques were infantile, based on simple Machine Learning algorithms that were trained to identify certain words and phrases in a sentence and then respond with fixed sentences. You must have seen this in chatbots on different websites.

More evolved applications of Natural Language Processing require advanced Deep Learning tools. The machine is exposed to a large set of labeled data for training to understand sentence breaking, grammar, semantics and many other features.

This is how programs are developed to understand and respond to voice and text human instructions. 

Applications of Natural Language Processing

Natural Language Processing has taken over a number of industries and augmented profits for a lot of businesses.

Let’s talk about 5 major ways in which NLP is impacting businesses.

1. Sentiment Analysis

With the amount of data available, manual analysis of data is becoming impossible in any shape or form.

Although with the volume of online businesses and social media exchanges, a lot of reviews and sentiments are expressed by consumers on different channels and they need to be addressed by business owners for reputation management, marketing analysis and opinion marketing.

These reviews or words are unstructured data. Since people write in a lot of different ways, no two styles of speaking or typing would be the same. 

Sentiment Analysis uses Natural Language Processing to define ‘polarity’ in sentences such as good, bad, happy or unhappy and proceeds with suitable responses based on the same.

A lot of food delivery apps, Airlines, Cab services and Manufacturing companies use NLP to help them deal with customer issues.

Once a text, tweet, mail, message or review has been flagged as negative, it is pushed to top priority and is immediately taken care of by the businesses.

2. Voice Recognition 

Natural Language Processing is also deployed as voice recognition across platforms. 

Voice and speech recognition helps programs convert speech-to-text to perform certain tasks. Everyday examples of voice and speech recognition are Google Assistant, Siri and Alexa.

Voice recognition has come a long way since its inception. The most common voice assistant, Google Assistant, claims over 95% accuracy which means that the word error rate for this assistant is only 4.9%.

Google, Apple and Amazon have made power moves by letting this technology become a part of our everyday life. With Home Digital Assistants like Alexa and Siri, playing music, playing games, setting up alarms, maintaining calendars, getting reminders and sorting tasks on your To-Do list have become much easier.

This has embedded itself in the minds of the consumers and has helped these business giants expand their presence in the market. Around 65% of 24-49-year-old people talk to their voice assistants at least once a day.

3. Chatbots

Chatbots are a bigger reality than we realize.

Gartner predicts that by the year 2020, an average human will have more interactions with a chatbot than with their own spouse. 

Chatbots have helped businesses exponentially reduce their turnaround time for both consumer complaints and lead generation.

NLP based tools help chatbots with breaking down sentences and semantically understand the inputs made by the consumers. 

In cases of lead generation, the chatbots are fed with standard responses and allowed to interact with customers depending on their queries.

You must have visited a number of websites where you’re greeted by a chatbot at the very beginning and that’s how the lead capturing works for many online businesses. 

Another popular application is that of customer grievance resolution. Chatbots are deployed across the web by service providers to resolve customer issues. 

Chatbots break sentences into phrases, match them with the loads and loads of data that have been previously fed to them and revert with suitable responses.

90% of businesses have reported that chatbots provide a positive resolution to their grievance handling system.

4. Advertising and Content generation

Natural Language Processing has overtaken many aspects of advertising as well. 

For example, Alibaba has introduced AI Copywriter for advertising businesses to help them create copies for product descriptions, slogans and other adverts. 

AI Copywriter is a Natural Language Processing based tool that is capable of generating 20,000 words per second.  

Such Natural Language Processing tools help with targeting keywords and audience correctly. NLP recognizes the interests and intents of the audience through their written words and help target them more effectively.

Natural Language Processing also helps in effective ad placements. Many magazines, newspapers and blogs roll out ads in their space. It is important for these ads to be relevant to engage the audience. 

Natural Language Processing helps identify these relevant pieces of content for these ads. For example, a tutorial article about home furnishing is better off with an advert of furniture on the page than a fitness ad.

By way of better keyword research, NLP helps companies save on their advertising budgets and target better leads with accuracy.

5. Market Intelligence

There is overwhelming content online these days. 

Sifting through millions of blogs, articles, news pieces, social media posts and e-mails can be daunting. 

Natural Language Processing uses summarization techniques and triggers to prioritize and discard these pieces of content.

Businesses have to be updated with the trends in the industry, changes in policies, competitor’s performance and how people are perceiving brands around them.

Natural Language Processing helps businesses keep track of changes in the market and industry and what is trending in their respective areas.

You can also use it to filter emails. With access to e-mails that pass through certain servers, Natural Language Processing can be used to identify spam emails and mark relevant ones in an order of priority. 

These are some of the ways the businesses are being transformed by NLP.

Natural Language Processing is a part of Deep Learning that is finding widespread applications in this day and age. Companies will continue to seek applications of Natural Language Processing to better their business processes.

We hope you now understand what Natural Language Processing is and how it all fits into the Deep Learning space.

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