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Applications of Natural Language Processing (NLP)

What is Natural Language Processing (NLP)

Natural Language Processing techniques, a subset of Artificial Intelligence increasing its necessity with the improvement of its sub-technologies day by day. Language is the prime source of communication and interactions. Without language, communication is not possible, and without communication, process completion is not possible. This is also another reason for the increment in the involvement of Natural Language Processing in different domains. The involvement of Natural Language Processing (NLP) is also increasing the dimensions of different areas. Some of these domains are -

These domains can be considered as use cases of Natural Language Processing but also have their separate use cases. The implementation of these use cases can be generalized to an extent, But these domains also demand diversities in the different levels of implementation as well as in the expertise required to implement them. Let’s understand briefly about these domains from the mirror of Natural Language Processing. One point is to be noted here that Natural Language Processing also requires some integration with other technologies such as Machine Learning, Deep Learning, and Big Data Analytics.

Natural Language Processing (NLP) Applications

Business Applications for Natural Language Processing

Let’s start with the domain of commercialization. It is pretty evident that the business domain is consists of some interesting and necessary use cases and problems that can be addressed by the use of Natural Language Processing. Some use cases of Natural Language Processing which are used in the Business domain are -

Sentiment Analysis — It is widely used in social media analytics and web monitoring which allows knowing the insights of the customers concerning particular products or services. It can be advantageous for any company to know about the thinking of the customers about a product so that they can know about the scope of improvement and how to achieve robustness. Natural language processing can not solely handle this task; it requires integration with highly computational methods such as Machine learning and deep learning to do the back end computation and Big data analytics to digest the data at an enormous scale.

Email Filters — Emails are adopted as a medium of communication officially now. Even the government considers it is official to communicate with the help of Email. But this medium is also vulnerable to spamming of the content. Companies that provide Email domains such as Google, Zoho or Yahoo are researching in the field of making it Full-proof by using different measures. Email filtering is an everyday use case of Natural Language Processing by applying various text analytics measures. It is a task of spam detection which is also in sentiment analysis as a pre-processing technique.

Voice Recognition — These are techniques that are powered by Natural Language Processing that allow companies to develop smart voice-driven services and interfaces for any product and service. To narrow the communication gap between the machines and humans is the most critical and necessary step to increase the grip on Artificial Intelligence. It can be achieved by only and only Voice Recognition which is possible by Natural Language Understanding a sub-process of Natural Language Processing.

Information Extraction — Information is the new fuel, it is a well-known fact now. But the data which is received at any receiving end mostly consist of unstructured format. The emergence of the advanced statistical algorithms results in the rise of predictive analytics and prescriptive analytics which made the prediction system more accurate. But these algorithms demand more and more information for finding the patterns and Matching them. Of course, Machine learning and Deep learning methods are doing an impressively great job, but without Natural Language Processing these things are not possible.

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