Introduction

With over one hundred million users in less than three months after launch. ChatGPT is a sensation in present times. Its growth and reach is attracting the attention of every technology pundit around the world. Suddenly the artificially intelligent world is looking closer and turning into a reality. Furthermore, increasing the value of machine learning and natural language processing in the growing technology world.

To explain artificial intelligence gives machines the ability to think on their own. Running on machine learning which is acting as a brain, algorithms are created and trained with the help of datasets to help solve specific problems. For example, self-driven cars, search results, business decision-making, or processing human texts.

AI combines technologies like Machine Learning, deep technology, neural networks, genetic algorithms, and NLP. Machine learning here acts as the brain of this technology, which teaches machines how to act and react according to the data given. Consequently, NLP takes advantage of this learning and detects human texts and answers accordingly.  

ChatGPT is one of the applications running on NLP. Users here send a message, and the machine analyzes and creates a response accordingly. In this blog, we will explore how NLP works, its requirements, and the different industries in which it’s present. Hence, giving you an idea of how you can use NLP in your business.

Understanding Natural Language Processing

NLP, or Natural Language Processing, is one of the most noteworthy innovations using artificial intelligence. By helping computers understand what is said and converting it into machine language, it creates a scope for real-time conversation with humans. Hence finding its usage across different modes be it in mobile applications, software, or websites. 

Fathering NLP is deep learning, machine learning, and artificial intelligence algorithms. To explain in detail, these algorithms first break down the text analyzed into clauses from where purposeless words are deleted. The remaining words are then changed to their root words, and then finally interpreted as per needs.

There are two common methods in NLP model creation. Where the first is human explained, where engineers carefully craft rules for the models to use. The other is the usage of Machine Learning for NLP, where models are trained using statistical methods for concurrent learning.  

Creating the scope of large-scale analysis and automation of repetitive tasks again. NLP models are present in almost every industry, including banking, retail, healthcare, and customer care. In brief, If AI is going to take over in the future, NLP surely then is going to play a role in it.

The Rise and Rise of NLP

Do you know that the first chatbot was Eliza in 1964-66? Yes! That’s right. Eliza was one of the earliest software built using pattern matching and substitution methodology. Constructing the foundation for more human-computer conversations technology building bearing fruits in present time. As a result more coming times watched the development of Parry and Racter. 

The transformation in the NLP got its boost by the integration of machine learning models after 1985.  As a result very rapidly showing the first success with much known Dr. Sbaitso. Afterwards, showing the results with Watson and Siri.

Concurrent recent innovations with supervised and unsupervised machine learning models have pushed the bars higher. Thereupon building models which can answer questions, find topics, summarize the text, and communicate with you anytime you like.  

Different Uses of NLP:

After knowing NLP, let’s explore some of its common usages. In the long run you apply these different uses in your industry and generate results.

1. Sentiment Analysis: The study of emotions behind the text is sentiment analysis. Notably used by companies with a huge customer base it helps in doing a statistical analysis of what is being said and making better decisions. Or, you can use it when you want your users to have an automatic response depending upon the kind of message they send.

2.Text Categorisation: Do you ever wonder how Google and Zomato classify the reviews into some pointers? No? Well! It happens with the help of text categorization. In other words they check specific keywords  and tag data according to that. An example of text categorization can be Shri Ram and Seeta, which will go to data like Hinduism and spirituality. In the technology world it is also called Named Entity recognition.

3. Virtual Assistant: While NLP doesn’t have any direct relation with chat assistants. It detects the text through voice commands given to the user with the help of semantic analysis. From where assistants generate appropriate responses, as the users require. To point out, Amazon’s Alexa, Google Assistant, and iPhone’s Siri run on this concept.

4. Easy Automation: Every business needs automation in their work, be it customer-facing or not. NLP knows this knotty situation and allows businesses to leverage their resources. As a result it has created bots who get training to analyze the text and then respond accordingly. Some use cases are ticket classification, spam detection, contract analysis, invoicing, and autocorrect. In contrast to chat bots, NLP can also help implement various other processes like forms and digital tele-calling.

NLP Applications:

To demonstrate, let’s explore some real-life use cases for the technology industry to check for its applications. Thus, showing the sky’s the limit to reach with proper application of NLP. 

1. Recruitment: Recruitment is a tedious task for companies for a long time. Particularly, reading through hundreds of resumes, looking at their experience and skill sets, and finally hiring. In fact, the biggest snag is cutting through irrelevant resumes. NLP solves this problem by detecting candidates with relevant skill sets and experiences. Additionally, many companies use it to map the internal employee skill sets and listen to their needs. For example, you can check out software honelt and remesh. 

2. Chatbots: If there is a single benefit of using NLP, it’s chatbots. Chatbots reduce the need for human-human intervention and help users find what they need in seconds. The most common methods of creating chatbot are: definition dependent and self-learning bots. To understand them better you can check out many different sources, some of the best sources specifically include IntelliTicks, Quickreply.ai, and Google Dialog flow. Or build one for you like different industries at present.

3. Social Media: With the increase in social media, there is an immediate need for surveillance. To stop users from sharing harmful content, spreading misinformation, and unsafe content in due time. NLP integration solves this problem for businesses. With NLP, they can detect messages, flag them, and even stop them. Your content is by going through an algorithm to get a tagging of being safe or not. Which ultimately, creates a safer and healthier environment for everyone using the platform.

4. Email Filtering: One of the first use cases of NLP in the tech industry is an E-mail filtering. Email filtering checks into incoming mail for users and categorizing them by phrases and words. For example you can look at the division by  an inbox, promotions, and social. Or by sending emails to spam folders, all is NLP. You can see that yourself next time when you check your email.

5. Autocorrect: Accomodate. Did you notice the spelling of accommodate is wrong here? Yes? No? Well, nothing new, misjudging the spelling is common. All thanks to autocorrect. Forming the core of our daily life messaging and chatting autocorrect has become a part of daily life. Its algorithms detect what is written and analyze each and every word. Thus, finding the errors and replacing them. 

Conclusion

Growing at a compound annual growth rate of 25.7% in natural language processing. NLP is going to be a part of every machine in the coming time. Furthermore, machines soon will have the understanding of not just words but also emotions. 

In fact, incorporate NLP into your business and start seeing the change from day one. But when doing that, always look for expert guidance. Finally, for a smooth you incorporation Contact Us today for your NLP projects!