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.
Read MorePosts about media & publishing
Understanding the Basics of Content Categorization
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.
Read MoreContent Recommendation Engine for Digital Publishing
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.
Read MoreHow AI Can Help News Websites Optimize Customer Experience
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.
Read MoreText Analysis Using Machine Learning
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.
Read MoreWhat is Data Labeling in machine learning?
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.
Read MoreUsing AI to Build a Google News Classification Project
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.
Read MoreAI in Digital Media and Marketing
The Skyl team gathered with data scientists and CEO’s this week to chat about the future of AI in the digital media and marketing landscapes. We learned more about the potential use cases for machine learning in these industries, as well as issues that teams run into while creating data science projects.
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Issues
Some of the issues that these marketing and media leaders talked about were:
- The inability for data science teams to properly communicate how timely and costly certain projects are to their managers
- It is difficult to gather data properly
- Buying labeled data, creating a project, and finding out that the data is not serviceable
- Properly integrating unstructured machine learning projects without a high initial investment
We guided them through Skyl.ai, and let them lead a discussion on how our platform could change the way that their business is conducted.
These use cases for Skyl.ai included:
- Sentiment analysis for public tweets
- Image processing to personalize the targeted marketing advertisements
- Natural language processing to learn new slang and find sentiments faster than their employees
Skyl.ai is a platform created for making the end-to-end machine learning workflow accessible for any person from business students to data scientists. Visit Skyl.ai to learn more about our platform and projects!