Humans are the most advanced species on Earth, and our success stems from the ability to communicate and share information. That’s where the concept of developing a language comes into play. The human language is one of the most diverse and complex parts of us, with over 6,500 languages that exist today. So, how do machines fare with the ability to process the human language?
In this post, we cover 8 everyday uses of Natural Language Processing (NLP), which is how machines are programmed to decode and understand the human language, to facilitate seamless interactions between people and technology.
What is NLP?
Natural Language Processing is what computers and smartphones employ to understand human language, spoken and written form. With increased access to smartphones and devices, NLP has become more of an integral part of life. While NLP can be a challenge to implement correctly, the benefits of the technology are manifold. Among the many advantages of NLP, is the ability to present unstructured data into comprehensive information, like enhancing customer experiences facilitated by virtual assistants that talk, think and act like humans.
Natural Language Processing (NLP), being a branch of artificial intelligence (AI), uses neuro-linguistic programming that helps computers to interpret, understand and manipulate human language for various applications. The NLP market is estimated to exceed almost 14 times larger than it was in 2017 by the year 2025 growing from approximately $3 billion to $43 billion by 2025.
NLP powers cultural exchanges
Beyond just making life easier, NLP has the powerful potential to bring people from all walks of life closer to each other, making information more accessible, learning more agile and cultural exchanges more globally inclusive. NLP is the core that powers virtual assistants like Siri, Google Home and Alexa. In fact, Google, Amazon, and Apple are all pioneering forms of voice-based NLP that covers more languages and regions from all corners of the world
Take the example of a Spanish TV show called Money Heist, where the intro song titled “Bella Ciao,” which is Italian, became a sensational catch phrase around the world due to the show’s popularity. An individual who knows not a single word in Italian or Spanish, can simply enter the words “Bella Ciao” into Google to find the actual meaning being, “beautiful hello.” This vast database of information and processing speed in real-time, is a revolutionary advancement in Natural Language Processing (NLP).
If you were to narrate a story to your Google Docs file while voice-typing is enabled, the NLP engine recognizes your tonality and word choice to turn your voice into text. You speak and the computer types it out quickly. Isn’t that amazing?
The software even suggests alternatives and possible influences that automatically corrects the language you narrate, with keen attention to grammar and spellings. The same process can be used to learn, teach, work, play, interact and a whole lot more.
Siri’s intelligent voice-based task management, Alexa’s IoT implications and Microsoft Bing’s Spell Check API are all NLP technologies we have gotten used to by now.
There are many more examples of NLP’s use in everyday life that you probably use right now. Let’s look at a few of them.
8 examples of Natural Language Processing (NLP) in everyday life
As the world comes closer with technology, the implications and uses of NLP are also increasing. Here are 8 everyday uses of NLP.
Faster typing with NLP
When you type out a message or search query on Google, NLP allows you to type faster.
1. Predictive text – Provides suggestions for the next word in a sentence
2. Autocomplete – Provides suggestions for the remaining part of the word
Text messengers, search engines, websites, forms, etc., utilize NLP technology simultaneously, to speed up the access to relevant information.
Precise writing with NLP
While writing an email, word documents, composing blog posts, or using Google Docs, NLP allows users to write more precisely.
3. Grammar checkers – helps users use punctuations, voice, articles, propositions and other grammatical elements by providing suggestions in your language of choice.
4. Spell checkers – help users remove spelling errors, stylistically incorrect language, typos, etc., based on the language chosen.
For example, Grammarly utilizes both spell checkers and grammar checkers to help you make corrections for a more accurate output.
Improved search with NLP
As you search for information on Google or Bing, various NLP algorithms allow you to find the right information quickly. The Google search is built using Query and Document Understanding at its core. Simply put, Query refers to your search input and a Document is the relevant web page or pages. Since the pages are in human language, NLP is vital to enabling search functions.
The miracle of NLP is that it happens within the fraction of a millisecond, without your knowledge of how it works.
5. Duplicate detection – classifies re-published content on multiple pages and sites to showcase a diverse set of search results for a given query.
6. Auto-correct – identifies the matching keyword in case you misspelled it or typed a less commonly used term.
7. Spam detection – eliminates spam pages that match your search query related keywords but do not offer a satisfying answer to your search query.
Constructive emails with NLP
Email users often face a lot of spam, and NLP can help defend you from spam. NLP engines can differentiate between various categories of emails and perform complex sorting to avoid malware attacks, phishing links, etc.
8. Email classification – Users of Gmail know this feature, where emails are separated into different categories like Primary (personal email), Social (Facebook notifications, etc.), and Promotions (company newsletters, subscriptions, etc.)
The many intelligent use cases of NLP are very helpful in saving time, reducing risks, and facilitating better access to information as well as human experiences. As the technology evolves with Artificial Intelligence and Machine Learning, more use cases like the use of NLP in self-driving cars, and NLP for IoT are becoming more prevalent. The use of voice assistant to communicate with your phone while driving is an early example of how NLP applications have caught up with daily lifestyle needs.
One of the most widely sought-after enterprise functions of NLP are conversational bots. Using AI powered virtual assistants, customers’ expectations and experiences are managed gracefully. The tremendous opportunity uncovered through digital transformation begins with NLP and branches out into various forms of artificial intelligence with practical applications in every day life.
If you would like to build your own intelligent, self-learning and human-like virtual assistant to automate your corporate communications, get in touch with our experts at Technovert right now.
The field of NLP is so vast that there are numerous advantages of employing this technology for the growth of human civilization.
Here are some FAQs to help shed light on the broad field of Natural Language Processing (NLP).
1. What are some popular NLP applications?
The NLP applications cover a whole spectrum of information from big data synthesis to simple text recognition and translation applications. To confine the rapidly evolving technology into a few applications is impossible. Some common uses of NLP include sentiment analysis to gather consumer feedback, named entity recognition from large sets of data, language translators, intelligent virtual assistants, voice command for IoT, etc. The diversification of NLP technology is further branching out into various domains like healthcare, automotive, finance and core energy sectors.
2. How does NLP work?
Fundamentally, the underlying technology of Natural Language Processing is an area in artificial intelligence that aims to develop a machines ability to understand, process and generate language like humans. Using binary code and algorithms, the machine is taught to fetch data, make conclusions and execute complex commands based on human behavior and patterns in speech. For example, if the Google search engine wants to identify the most used term for any goods or services, the system must analyze large sets of search queries to match the most widely used phrases from vast sums of data. Observing these patterns requires an in-depth understanding of the language and human condition This is an example of how NLP helps understand consumer behavior and is improved over time to produce innovative solutions to people. NLP works by altering what we believe and how we behave based on those beliefs. By imitating human thoughts, feelings and behaviors, NLP can help human control their destiny.
3. What are some popular NLP tools?
NLP technology is advancing fast, and the vast amounts of research over time has resulted in groundbreaking access to the latest NLP tools. Students, professionals and hobbyists can all gain access to cutting-edge NLP tools owing to the widespread innovation in this field and the contribution to open AI that anybody can use. The ability to develop and code NLP applications from the scratch has also become easier with new languages and libraries available in open source, where technologists experiment and share ideas.
Some popular NLP tools for various functions and applications are as follows:
A) Natural Language Toolkit
Platform for Python programs to work with human language, accelerating the development and deployment of innovative NLP applications.
Easy to install APIs and libraries of resources to build products by leveraging the latest NLP applications.
C) Berkely Neural Parser
A high-performance NLP parser with 11+ language models and based on the Python language.
Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that employs deep learning to generate human-like text.
Allen NLP focuses on the research, performance and accountability of NLP AI. By advancing scientific methods to evaluate and understand NLP systems, their work continues to serve humanity with NLP solutions.