Current Applications and Future Possibilities of Natural Language Processing (NLP)

Current Applications and Future Possibilities of Natural Language Processing (NLP)

The world of technology encompasses and touches upon our daily lives in various aspects. With the escalating amount of data being generated in the day to day across the globe, there is a critical need for Natural Language Processing.

In this article, we are trying to provide a brief introduction about NLP and in-depth about the current applications and future possibilities of Natural Language Processing.

Natural language processing (NLP)

NLP is one of the most useful technologies available to artificial intelligence systems. The technology enables machines to read, understand, analyze, and parse human language by using a plethora of complex algorithms.

How Does NLP Works?

Whenever we read a text, our brains are deciphering a progression of words and making associations. Those human capabilities that enable us to comprehend language are the ones that Natural Language Processing attempts to reenact and pass on to machines.

NLP works by separating words into their simplest structure and recognizing rules, patterns, and relationships among them. NLP will make use of a combination of computer science and linguistics. Linguistics is used to comprehend the meaning and structure of a text by analyzing various perspectives like grammar, pragmatics, morphology, and semantics. Later, computer science converts this linguistic content into machine learning algorithms that can take care of specific issues and provide the desired output.

Where do we can apply Natural Language Processing?

NLP is the driving force behind various applications, which we are using in our daily life.

· Word Processors like Grammarly and Microsoft Word that employ Natural Language Processing to check for errors in spelling and sentence structure

· Language translation applications like Google Translate

· Personal assistant applications like Siri, OK Google, Alexa, and Cortana

· Interactive Voice Response (IVR) apps are widely used in call centers to answer specific customer queries.

Current Applications of Natural Language Processing (NLP):

(i) Sentiment Analysis

Sentiment analysis is also called as emotion Artificial Intelligence. It is a kind of data mining that measures the tendency of individual opinions. The task of this evaluation is to discover personal records in the text.

Sentiment analysis will help me to check whether customers are satisfied with the services or products. The search for negative comments and the identification of significant complaints appreciably helps to enhance advertising and marketing.

(ii) Machine Translation

Machine translation (MT) refers to completely automatic software that can translate content from one language to some other languages. The perfect example of MT is ‘Google Translate’.

Machine translation is also known as instant translation or automated translation. It will even translate the essential data and speech into any other languages, without any human involvement.

The machine translations are divided into four types

ü Rule-based Machine Translation (RMT)

ü Statistical Machine Translation (SMT)

ü Neural Machine Translation (NMT)

ü Hybrid Machine Translation (HMT)

(iii) Speech Recognition

Speech recognition technology has been around the world for the past 50 years. Though scientists are trying to solve this problem, Natural Language Processing (NLP) only achieved success in the last few decades. Today, speech recognition is a trending topic that is part of various products, including voice assistants (Siri, Google Assistant, Cortana, etc.). This fascinating application uses to replace other time taking methods like typing, selecting, or clicking the text in any other format.

(iv) Chatbots

Chatbots is one of the best solutions for consumer frustration regarding customer care call aid. They have been offering modern-day virtual assistance for common issues of the customer. Chatbots will help to minimize human efforts, cost, and provide efficient solutions that save time. Soon, intelligent Chatbots will be providing personalized assistance to users.

(V) Automatic Summarization

Automatic summarization is a method of creating a short, precise, and articulate summary of a longer text document. It comprises of diminishing a text and creating a compact new form that contains its most applicable information. It will be especially helpful to condense large bits of unstructured data, for example, academic papers.

There are two distinct methods for utilizing NLP for Summarization: the foremost extricates the significant data within a text and uses it to outline (extraction-based summarization);. At the same time, the second applies profound learning strategies to paraphrase the content and produce sentences that are not available in the primary source.

(Vi) Spell Checking

Spell Checker is the most commonly used software tool by employees in every organization, especially the Text Editors or Content Developers. This awe tool recognizes any spelling errors in a sentence and rectifies it with correct spelling. One of the most realistic examples of the spell checking tools is ‘Grammarly’ Application. Grammarly is an online (Free/Paid) grammar checker that examines your content for a wide range of errors, from grammatical errors to sentence structure mistakes and past.

(Vii) Question replying (QA)

Question-Answering is turning out to be increasingly more popular thanks to the apps like Siri, chat boxes, virtual assistants, and OK Google. A QA application is a framework prepared to answers the questions raised by humans. It might be utilized as a text-only interface or as a verbally expressed system. While they offer an incredible guarantee, they have a long way to go still. It remains a significant test, particularly for web search engines, and is one of the principles uses of NLP.

Ø Future Possibilities of NLP

As technology continues to grow, future applications of Natural language Processing will be more user-oriented.

For example, virtual assistants can solve many complicated queries assessing the implications alongside with the literal, which means of the query asked. The NLP applications are not just restricted to resolve customer questions or offer customized shopping but have, however, has advanced into a greater technological help of sorts. In the present day, Natural Language Processing can be trained to give a list of mistakes, if someone uses NLP to ask, “What’s wrong with my network?”. In the coming years, NLP will be in a position to find out the user’s actual intention like she/he wants his network constant for access.

The future with NLP is thrilling as advances it will permit human to shift focus from the questions to the answers. In the exciting days yet to arrive, NLP will be built-in with different technologies such as gesture and facial recognition to enterprise revenues and make them more efficient and agile.

Final Words

Natural Language Processing (NLP) is changing how we analyze and connect with language-based information, by making machines equipped for comprehending content and performing human tasks such as summarization, translation, characterization, and extraction. Besides, NLP giving organizations a fantastic chance to analyze the unstructured information, including customer support interactions, product reviews, and social media posts, and acquiring valuable insight into the targeted clients/customers.

A few years ago, the way computers understand human language appeared to be unimaginable. However, in a short span, Artificial Intelligence, and Machine Learning — Natural Language Processing (NLP) has become one of the prominent and fastest-growing fields.