October 13, 2021
Best Uses Of NLP In Today’s World
According to research, on average, a human speaks around 7,000 words per day, which includes words of actual value (i.e., words that have an intent to communicate to other people). Just think about what these 7,000 words mean to you, as the words you share each day are your genuine imprint on the world. They dictate how other people perceive you and broadly define you. And here, we’re not referring only to words you use in verbal face-to-face communication, but also words you’re using in phone conversations, instant messaging, and so on, as authentic ways people exchange their thoughts and information in the modern-day digital era we currently live in.
In conformity with the Wernicke-Lichteim-Geschwind model, the human mind perceives the words through a unique word reception center entitled Wernicke’s area, positioned in the left temporoparietal junction. Afterward, Wernicke’s area projects the words to Broca’s brain area, which is called the word production center and is positioned in the frontal lobe. So, when someone asks, “How are you doing?”, the words are received by Wernicke’s area for interpretation, and further, Broca’s area of your brain picks the words for a response like “I’m doing great, thanks for asking!” Moreover, our brains are supported by millions of neurons to understand words and take further action.
So, have you ever wondered how systems and machines understand and process human languages and then respond with the right set of words? For instance, when you talk to voice assistants on your phone, say Apple’s Siri, it definitely comprehends your words, commands, and intent. Moreover, it offers a very satisfactory response. Well, the technology behind machines being able to communicate like regular people is called natural language processing, or NLP in short.
NLP is a subspecies of artificial intelligence, linguistics, and computer science involved with the interactions between humans and human languages from one side and computers on the other side. Natural language processing allows machines to process what is said, structure the received information, identify the adequate response and then respond in the language that the human user understands. And nowadays, with the increased use of artificial intelligence-powered technologies, natural language processing benefits from similar popularity.
Voice assistants like Siri are not the only excellent use case of NLP, as this technology has a plethora of applications in today’s world. However, even if you can’t realize it yet, there are countless real-world examples of NLP uses that significantly impact our everyday lives. In the latter segment of this blog post, we will discuss the best uses of NLP in today’s business world and how it helps break down human languages in a format that’s entirely understandable for both people and complex computer systems. Let’s get started.
Top 10 Uses Of NLP In Today’s World
Natural language processing-powered software like the Y Meadows model can help businesses analyze data better, discover valuable insights, automate time-consuming processes like ticketing systems, and help them gain a competitive advantage over their market rivals. Below, we’ll take a look at ten of the most exciting uses of NLP in today’s business world.
Machine translation is a subcategory of computer linguistics that includes utilizing NLP-powered software to translate speech or text from one language to another. One of the finest examples of machine translation is the ultra-popular Google Translate, which translates over 100 billion words each day.
Google Translate utilizes neural machine translation that uses artificial neural networks like deep learning to translate the complete sentence at a time. In addition, it utilizes an example-based machine translation approach where the system learns from hundreds of millions of examples to improve the final output. However, while the ambiguity of human languages makes it challenging for the systems to understand the words, sentences, and their intent, natural language processing application helps overcome these difficulties.
Have you ever asked yourself while speaking to your Google or any other voice assistant about how they recognize your voice, accurately interpret the meaning of your words, and give a sufficient response? This is possible thanks to the interdisciplinary subfield of NLP, computational linguistics, and computer science that creates methodologies and technologies that permit the recognition and translation of spoken language into text by computers.
Or in other words, speech recognition is a groundbreaking technology that permits computers and machines to convert human voice input data into a machine-readable format. Speech recognition has various applications, including with virtual assistants, adding speech-to-text, translating speech, sending out emails, and more. Thanks to NLP, speech recognition is also frequently used in search engines so that the user can voice out the name of their search specifications and get the wanted result, making it way easier than typing out the entire command.
Unstructured text is everywhere around us. For that reason, to organize and structure various social media posts, websites, emails, chat conversations, and others, NLP-powered text classification is an essential component for organizations and is rigorously utilized. Text classification, another example in the long list of best uses of NLP in today’s world, works with the help of classification algorithms that lay down the basis for systems to process text data on a large scale.
For instance, email clients utilize text classification for tagging emails to particular categories so that they can be sent to the inbox, spam, or draft folder. Also, the categorization of emails as primary, promotions, or social in Gmail software is a prime example of text classification using NLP.
Another use case of NLP text classification is within online discussion forums. In such places, text classification algorithms determine if a comment needs to be flagged as inappropriate. Along with that, eCommerce companies, news and marketing agencies, content curators, and more use text classification to automate their product, service, or content tagging.
One of the most widely recognized uses of NLP is grammar checking. This software detects and corrects grammatical errors in a piece of input text, helping in-text authoring, language learning, and many other scenarios.
While people can always check for grammatical errors manually, an NLP-motorized grammar checker would be faster and more effective in the quest to note any significant grammatical mistakes and misspellings and would offer suitable suggestions to correct these mistakes.
For instance, all leading writing tools such as Grammarly, ProWritingAid, and WhiteSmoke, heavily rely on this use of NLP to correct spelling and grammatical mistakes. In addition to grammatical assistance, this NLP-powered software can check the clarity of each text and suggest better synonyms. This means that this NLP application can help users improve their texts’ readability and convey their messages in a more professional, better manner.
Chatbots And Virtual Assistants
Having a solid customer support system and creating exceptional experiences for their customers is a goal for every thriving business. Nowadays, companies have specialized customer service departments that consist of tens or hundreds of human customer service representatives. However, engaging human representatives to address customers’ demands and requests day and night may pose various challenges. This is the primary reason why chatbots and virtual assistants exist these days.
Chatbots and virtual assistants are replacing traditional customer service agents and companies’ FAQs. Nevertheless, not all chatbots are created equal, and there’s a big difference between whether the chatbot is an outdated, rule-based model or a sophisticated, NLP-powered one. The latter one can efficiently interact and converse with people in the manner a human representative would. While antiquated chatbots fail to comprehend the customer’s intent because of their restricted capabilities, chatbots powered with the latest Y Meadows NLP solution can do a much better job.
Customers interacting with sophisticated chatbots may not even realize that they’re not speaking with an actual human being due to the perfect impression they get from these chatbots, thanks to their advanced natural processing capabilities.
Search Autocorrect And Autocomplete
Another significant utilization of natural language processing is the so-called auto-complete function. Whenever you start your search query on any search engine, you will get numerous predictions of what you may be interested in based on the initial few characters or words you’ll enter in the search bar. So, how do the most famous search engines like Google suggest relevant keywords and autocomplete your queries?
Ultimately, they rely on their data based on what the other billions of users are searching for when typing similar search terms. This is possible only by using NLP as it helps the search engines understand subtleties between different search terms.
Autocorrect is another open application of natural language processing technology, as it can suggest the closest possible terms to misspelled words and change them thanks to this function of NLP.
Nowadays, with so many factors whirling around, business decisions are difficult to make, and the best way to make informed business decisions is through data-driven insights. However, since businesses create large amounts of data, extracting meaningful insights may be challenging and may take a lot of time.
The collected, unstructured data can be easily interpreted using natural language generation tools. Executives can consult analytical dashboards powered by natural language processing and natural language generation to get the most recent information and help them make the best decisions for their business. Many companies rely on NLP to perform text analytics and identify data patterns, especially contract reviews, and social media sentiment analysis.
Optical character recognition is the technique of converting images of typed, printed, or handwritten text into machine-encoded language. It’s one of the best uses of NLP to digitize printed texts so they can be stored, edited, and searched electronically. As a result, NLP and OCR combine to provide a wide array of benefits that can help companies complete their document-related tasks like reading and recognizing passports or ID cards, enter data into different forms of CRMs, verify customer data collected from multiple sources scan cards, cheques, tickets, etc.
Sentiment analysis is the procedure of classifying and interpreting customer emotion within written text data. Generally, sentiment analysis helps companies identify their customers' sentiment towards their products, services, or brand in the form of online feedback, which can be positive, neutral, or negative. Thanks to its exceptional abilities, sentiment analysis is an extensively used tool for market research, product analysis, reputation management, public relations, precision targeting, NPS, and more.
In this highly competitive business world, companies of all sizes find themselves on the receiving end of hundreds of CVs and resumes from different candidates. As a result, it has become quite challenging for HR teams to go through all applicant resumes and choose the best-suited candidate for a particular position.
NLP can make the process easier by filtering through all of the resumes and shortlisting the candidates by different techniques like information extraction or name entity recognition. This process goes through additional attributes like skills, education, location, etc., and selects the potential candidates who meet the requirements of the company the best.
The Bottom Line
As you can see, natural language processing has many exciting applications in today’s business world. It helps businesses process vast amounts of unstructured data, gain valuable insights about their clients, and offer multiple benefits to the end-users of the service. Because the ambiguity affiliated with human languages is one of the biggest challenges and perks of NLP, understanding your business’s requirements, training the algorithms right, and then combining the NLP with other artificial intelligence technologies is the key to overcome the challenges your business faces regularly.
If you’re keen to know more about how AI and NLP can help instantaneously transform your business for the better, click here to set up a free consultation session with our experts and set your business for success in the years to come.