Four Awesome Applications Of Natural Language Processing (NLP)

Just think about the amount of text and voice data that we send or receive each day. Why not make this data meaningful with this huge amount of data and doing something cool? Now we have programs which can use our language to perform extra cool functions. Such systems are based on a combination of artificial intelligence and computational linguistics and are together under the term NLP — Natural Language Processing.

A Tractica report on the demand for natural language processing (NLP) predicts that the total market size for NLP software, hardware, and services will be at least $22.3 billion by 2025. The study also estimates that AI-leveraging NLP software solutions will see global growth from $136 million in 2016 to $5.4 billion by 2025.

Applications of NLP

Let’s see some of the best current applications of NLP that are changing how we interact with our machines and programs.


These days we hear a lot about Chatbots, they are the answer to user dissatisfaction when it comes to customer care call support . They offer modern-day virtual assistance to customer’s simple problems and discharge low-priority, high turnover tasks that do not need any skill . Intelligent Chatbots will provide consumers with customized help soon. If you have tried online shopping or interacted with a chatbox on a website, you interacted with a chatbot rather than a human being . These AI customer service gurus are in fact algorithms that use natural language processing to understand your query and respond ina appropriate, automatic, and in real-time to your questions .

Machine Translation

The concept behind MT is to build computer algorithms for automatic translation, without any human intervention or need . Google Translate is the best-known tool yet. Google translate is base on even an NLP field called statistical machine translation (SMT) . As simple as it may seem, it is not a word-to-word replacement. It collects as much text as it can which seems to have a similar meaning between two languages, and then it analyzes the data to find the likelihood of that word used in the same meaning in the other language . And this is analogous to us humans, we begin to divide semantic meaning to words when we are young, and we interpret and extrapolate these semantic values with given word combinations .

Market Intelligence

Marketing agents also use NLP to find people with potential or clear intention to make a sale. Internet behavior, using social networking sites, and search engine requests provide a lot of useful unstructured customer data . Selling the correct ad for internet users helps Google to take full advantage of its sales. Market intelligence at its heart uses many information sources to build a broad picture of the existing market, consumers, challenges, competition, and growth potential for new products and services . The raw data sources for this analysis include sales logs, surveys, social media, and many more.

Read full Story at on February 3, 2020.