Named-entity recognition (NER) also known as entity extraction is a subtask of information extraction that aims to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, places, expressions of times, quantities, monetary values and more.

We generally categorize everything that we come across in life, for example, we group books, music, and movies into specific genres based on their special characteristics. Digitization has helped apply the same concept to online content such as web pages, blog posts, news, e-books, and other learning content. With the immense increase in online availability of information, data needs to be found easily and analyzed faster. This is where content categorization steps in.

As a digital publisher, you constantly face the challenge of attracting a loyal online audience to build a successful brand. The best way to address this problem is by providing impactful and personalized content that keeps the user engaged. Most publishers use content recommendation systems to enhance user experience, increase page views, and improve site ranking. 

News websites are constantly fire fighting on all fronts to deliver quality content to their user base and to keep ahead of competition. It is 2020 and AI has proven to be a game changer in many business verticals. Here are some futuristic ways AI can help in optimize their online customer experience and increase their revenues.

In this article, we will see how to use machine learning for Text Analysis. Natural language processing is a branch of AI and machine learning that aims at extracting meaning out of text by using machine learning algorithms.

When you are embarking on a machine learning project, data labeling is one of the key components that will determine the success of the project. One of the important steps in machine learning projects (especially in unsupervised methods) is data labeling which is a process of augmenting raw data with a set a meaningful tags.

Increasingly, digesting and reading news has become a standard in our day-to-day lives. Gone are the days when you get a single update in the evening after work while watching television before bed. People are now bombarded with a constant stream of news updates throughout the day, whether they want to be or not. Headlines are splattered over every form of social media, breaking news is sent to us in alerts, and if we ever need to seek out what is happening, it is a search away. However, it is possible to create your own “Google News” app using machine learning. By teaching an algorithm to automatically sort through headlines from the thousands of possible news sites there are, a business can filter through exactly which content it wants. There are multiple reasons a business would want to do this. Maybe, a veterinary clinic wants to automatically keep updating their website with feel-good stories about pets and babies and wild animals. On the flip-side of the coin, large corporations may want to save time by having an automated system that sends their employees the latest news real-time about finance, politics, or any other topic that the company may find relevant.