One of the fascinating areas of AI is Natural Language Processing (NLP), which has already produced many tools that impact our daily lives. Tools like chatbots, voice assistants, or translators are direct results of the advancement of NLP.

1950 was the beginning of the study of natural language processing. When scientists made the first effort to make a machine translator that could translate from Russian to English. To determine if a machine might behave similarly to a person, the Turing Test, originally known as the imitation game, was developed at about the same time as the machine translator. Since then, NLP has advanced significantly and is constantly improving to comprehend human language’s complexities, context, and ambiguities with the growth of unstructured data.

In this post, we will look into the areas of Natural Language processing that have been moving the fastest in recent years.

Service Desk Intelligence

Today, if you call a service desk with a problem, you usually get a response in the form of a ticket that has been opened, and you’ll hear back within a time set. However, according to studies, most tickets are recurring and may be resolved without assistance if organizational knowledge is appropriately collected. In this case, NLP is used to quickly and effectively resolve the problem without outside intervention. According to the subject and content of the email and the ticket’s category, the system searches through previous encounters before deciding on the ultimate resolution method. The NLP system’s initial reaction will be to provide the user with a detailed action plan, and it will then follow up through email with a virtual assistant who can help them solve their problem immediately. Response emails become more intelligent in this way, enhancing the consumer experience with the company.

Adaptive Learning

A model is trained for one job and then reused for another activity related to the primary task using the machine learning process known as adaptive learning. Therefore, you will need to tweak a pre-trained model rather than create and train a model, which is costly, time-consuming, and requires enormous quantities of data. So as an alternative method, by employing fewer labeled data, firms may execute NLP tasks more quickly.

So adaptive learning, which first gained popularity in computer vision, is now employed in NLP applications, including named entity identification, sentiment analysis, and intent categorization.

Workplace Attitude

A workplace’s emotion, toxicity, and popular subjects of discourse may now be appropriately assessed in real-time by modern natural language processing algorithms. However, the workforce views this with distrust, but senior management possibly values it. Anonymous software may read, analyze, and assess every word when communicating via teams, phone, and email. Such methods are undoubtedly susceptible to interpretation.

At the beginning of the lockdowns, workplace spying technologies became widespread. Management began to suspect that no one was working since everyone was home. Thankfully, common sense finally won over in the majority of enterprises. These technologies are now mainly used to track average employee behavior and only highlight the worst violators. However, NLP can advance them to a new level. Executives using these techniques may see the cooperation climate inside a business. With these apps tracking toxic behavior inside an organization may serve as a crucial performance indicator for managers promoting well-being, engagement, and corporate culture.

Low-Code Tools are becoming popular

In the past, coding expertise required a strong foundation in coding, open-source libraries, and expertise in machine learning if you wanted to develop an NLP model. That’s not the case anymore. Low-code and no-code technologies have been available for a while. But they’re expected to become more widespread in the next years. In addition, NLP activities previously solely available to data scientists and developers may now be performed by non-technical users. Thanks to SaaS firms like MonkeyLearn, Quickbase, Nintex, etc. 

By making it simple to create, train, and integrate text classification or sentiment analysis models in just a few clicks using low code tools (point-and-click model builder). We may anticipate an increase in the use of NLP tools by organizations.

Machine Translation

Natural language processing may be used for translation on the internet, as implied by the name of the technology. On the internet, a vast amount of data is accessed by people from different parts of the world who use many other languages. NLP aids in the translation of those languages into users’ native tongues. Because of the diversity in the number of users who speak and comprehend various languages. Nowadays, NLP for machine translation is adopted by many businesses, among which Google is the leader. Maintaining the original meaning of the translated text is the most challenging issue, while translation entails a unique set of difficulties. These difficulties are being addressed by running advanced NLP methods on large amounts of collected natural language data. 

Media Monitoring

Consumer opinion is crucial to a brand’s success in the contemporary corporate world. Customers can freely express their views about the brands, services, or products on social media and other online forums. As a result, modern firms seek to monitor brand references online. The application of machine learning has given these monitoring efforts the most significant boost.

In your social media stream, for instance, the analytics tools like Keyhole, sprout social, Hubspot, etc, can filter every post and present you with a sentiment timeline that shows the postings’ positive, neutral, or negative opinions. An ML-backed search across news websites is also comparable to this. Industries and markets sensitive to public perception may use NLP to analyze digital news sources to determine how people feel about their business.

Customer service may be enhanced by such media analytics. For instance, financial service providers can keep an eye on pertinent news events (like oil spills) and acquire insights to help clients who have investments in that sector.

Chatbot

One of the most widely used NLP applications nowadays is chatbots. They have multiplied a great deal in recent years. When a robot can respond just as effectively, why use a human? 

According to Rachel Roumeliotis, Vice President of AI and Data Content Strategy at O’Reilly Media, “NLP has risen in popularity for its capacity to power applications that are proven to be beneficial for organizations, from customer service chatbots to medical hotline lines.” Today, NLP automates several jobs for regular employees, including auto-filling written work or lines of code.

Predictive Text Generator

One of the intriguing NLP efforts is predictive text generators. Do you know about the video game AI Dungeon 2? It is a paradigm for developing a text adventure game using the GPT-2 prediction model. The game is based on an interactive fiction archive, and by creating open-ended plots, it shows off the marvels of auto-generated prose. While machine learning in game production is still in its infancy, it is expected to revolutionize experiences shortly.

Another example of automatically generated text is DeepTabNine. It is a code autocomplete for many programming languages powered by ML. You may add it to your IDE to get quick and precise code suggestions. People can now develop their own NLP tool GPT-2 model from Open AI. It’s a complete pre-trained model that can be implemented and interacted with relatively quickly. 

Identifying Scammers

Spam filters are employed in natural language processing. This widely utilized protection technique is crucial for identifying spam emails and communications. We all get many emails we don’t want to reply to because they might compromise our security. Separating the useless emails from the ones we need to check becomes a complex process. NLP can muffle these emails and separate them from the essential ones. Prominent email service providers have now made it part of their basic service.

A Customer Support Bot

Working on a customer care bot is one of the finest ways to begin experimenting with your hands-on NLP projects for students. A typical chatbot provides pre-written responses to common client questions and requests. These bots, however, need help understanding more complex queries. Support bots are outfitted with artificial intelligence and machine learning technology to overcome these constraints. They can create replies to the questions without pre-written solutions and comprehend and compare human inputs.

Conclusion

Natural language processing (NLP) applications are expanding at a breakneck pace, and NLP is undergoing fast development. With so much data at our disposal, it’s critical to comprehend, watch over, and filter it to get its real benefit.

The availability of low-code, no-code tools and ready-to-use pre-trained models will help NLP grow even more in the following years. Businesses will continue to gain from NLP, from better operations and customer happiness to cost savings and better decision-making.

Companies need more assistance than ever to process vast amounts of data, and NLP is currently the best solution.