The binary language was invented to bridge the gap between human and machine communication. From the time we as humans worked on understanding computer language, we have gradually evolved to a phase where we are currently coming up with breakthrough technologies on making computers understand human language, emotions and sentiments.
This breakthrough tech is called Natural Language Processing or NLP.
As the result of dynamic artificial intelligence development, NLP is responsible for most of the innovations we experience in industries and our everyday lives. In fact, some of the NLP applications in real life are so seamless that they have already become an integral part of our lives.
That is also why the NLP market has been consistently becoming inevitable.
Statistics reveal that in 2017, the NLP market was valued at around $3bn. By 2025, experts believe that this market value will increase by 14 times, making it a $43bn industry in the coming years.
A Quick History of NLP
Natural language processing appears futuristic and new but it would be hard to believe that the foundations for the technology were laid in the 1950s. Artificial intelligence is a term coined in the 50s and it was during the same time that Alan Turing also conceived and developed a test for machines that could think.
From the time experts started observing languages as systems, consistent developments and advancements allowed researchers like Naom Chomsky to develop a concept that could translate natural sentences into computer understandable formats in 1957.
After a brief hiatus, NLP emerged again in the 80s with newer ideas and concepts. As tech kept evolving, we gradually developed systems and hardware peripherals that could eliminate all hindrances associated with data generation, storage and processing. With Siri, we witnessed a revolution in NLP and machine learning concepts and there has been no turning back ever since.
With NLP gaining grounds, more light had to be shed on the innovations and applications made possible by the technology.
So, here is a detailed post on the uses of natural language processing handpicked and curated for understanding.
Let’s get started.
10 Real-world examples of Natural processing language
Experts believe speech recognition is the future and the dynamics of human-machine interfaces would completely change with more integration of voice recognition. This futuristic vision could be driven only by a technology like NLP that converts human language into a format that is understandable by machines.
Today, we have virtual assistants that can understand our moods, emotions, preferences, modulation and more from the way we talk and come up with human-like responses artificially. Apart from these, NLP is also used by businesses to transcribe calls, recordings, voice notes and other audio-based data and even translate them into other languages.
Statistics also reveal that close to 50% of all search queries would be voice-based in the coming months and years.
When humans read, we tend to relate context, situations, sentiments and other abstract concepts to the text and comprehend what is written. Machines, on the other hand, cannot do that. Semantic analysis is the process of making machines understand the semantics associated with a text to comprehend sentences, interpret emotions, analyze grammar and sentence structures and correlate phrases with situations.
With companies and businesses generating massive volumes of unstructured data every day, NLP with its semantic analysis application can help organizations make sense of text-based data for business decisions and insights. This could be as simple as understanding a customer email to understanding sarcasm in social media reviews.
Apart from digital unstructured data, there also exists immense volumes of data that is paper-based or offline. In the form of offline files, documents, invoices, bills, receipts, journals and more, businesses have tons of paper-based data that need to be digitized for diverse reasons. NLP can help ventures do this by Optical Character Recognition systems. These systems can convert print text into digital formats that can then be stored, accessed and retrieved anytime. From customer data to CRM data, this helps businesses save operational time.
Conversational User Interface
A conversational user interface is an element that can take machines a step closer to cracking the Turing Test. These interfaces prevent chatbots, robots and other digital AI assistants from sounding like machines and bring in a human element in conversations. The enhanced form of IoT development process allows businesses to connect better with their customers and leads through conversations that mimic human behaviour. An optimized conversational user interface can help customers execute a number of tasks including:
- resolve conflicts
- book flight tickets
- raise customer support queries
- initiate refund of financial transactions and more
Smarter Ad Funnels
Generic advertising has become obsolete and businesses are looking at optimized methods of targeting and reaching out to their potential customers. To do that, advertisement funnels are being deployed to identify, classify and understand the diverse needs of customers at specific phases of their conflict-resolution process.
With NLP, precise levels of detailing in targeting can be achieved in marketing with the help of their digital footprints. Thanks to this, businesses could seamlessly identify what campaign or content would be the most apt for customers and at what time. This increases quotients like visibility and credibility and makes businesses generate revenue.
The performance of a business is dependent on a number of factors. From competition and volatile consumer behavior to government regulations, several factors consistently bombard businesses, pushing them to make decisions to stay afloat.
With NLP tools, businesses can ensure they are always abreast with market happenings with the availability of precise information from credible sources. From keywords and phrases on what consumers think about their business or that of their competitors, companies can leverage on an opportunity or convert a threat into an opportunity and make way for a stronger market presence. This data can also be then used as historic data to predict probable outcomes in the future.
Improve Customer Service
NLP in Artificial Intelligence solution has allowed algorithms to clearly understand human emotions and intent. This is of immense help in optimizing customer service. NLP can enable businesses to deliver better customer service and resolve conflicts by understanding what callers, customers or visitors want. From understanding their concern and helping them tackle it to managing online reputation, customer service can be efficiently managed with the help of NLP.
Predict and Classify Text
Text classification is the process of tagging unstructured data and classifying it for better understanding and processing purposes. While manual efforts could take hours of time, NLP can help businesses classify massive volumes of text in minutes. This could be implemented in making customer services more efficient, understanding survey responses, analyzing market sentiment and more.
On the other hand, text prediction allows users to type less and convey more with suggested sentences and phrases. NLP can now understand the emotion behind phrases and predict what users are most likely to type at a given instance of time depending on users’ moods, behaviours, previous responses and more. With these parameters, NLP modules seamlessly suggest words, phrases or even emojis for responses that users can select. Gmail recently introduced a similar system as well.
Delivering error-free content, email, response or even a text has become a very casual affair, thanks to the spell check functionalities offered by NLP. It allows systems to detect incorrect spellings and automatically change them. From crucial business reports to school assignments, everyone can now submit a clean, error-free document.
Social Media Monitoring
Opinion mining is a more precise term we could use for social media monitoring with NLP. With the internet flooded with opinions, it is a treasure chest for marketers and brands to uncover new opportunities, look for scopes to improve their products or services or just listen to their customers. With NLP, sentiment analysis and opinion mining are now possible. With this, businesses can also evaluate the effectiveness of their campaigns, assess customer reactions and even predict them.
The real-world applications of NLP are not just restricted to the instances we discussed so far. They are being increasingly deployed to fight cybercrimes, prevent online bullying, detect fake news and hoaxes, optimize reinforcement learning and more. As far as NLP is concerned, we are only getting started with scratching the tech’s surface. Let’s wait and see what exciting applications NLP offers us in the future.