<img alt="" src="https://secure.agile365enterprise.com/790157.png" style="display:none;">

Smart Content Tagging Process To Relieve Content Marketers

Adding tags manually to the content is tedious and inefficient while automating it gives search audiences a convenient way to dig deeper into related stories and also ease the work of content managers.

Highlights:

  • The tool helps generate tags and entities automatically, using Machine Learning and Natural Language Processing
  • Saves money and time of content creators with tidy content organization, and allows audiences to search the articles with just one tap

The Challenge

Content creators add tags to every new content piece manually before pushing it live, making the process prolonged and error-prone. Adding wrong (meta) tags makes it difficult for search engines to crawl and index content adequately, forcing organizations to witness a sharp decline in organic traffic or traffic that doesn't convert. 

The Solution

Srijan has built a tool that automates the tagging process to enrich content with contextual tags for fostering content discovery. The tool utilizes supervised learning, machine learning, and NLP to generate relevant tags and entities for a given content piece. The ultimate goal is to produce meaningful, reliable, and discriminating tags automatically, that are vital for information retrieval. 

smart tagging tool output

Explore how this smart content tagging tool works

 

The technologies that have been used to build this solution are-

  1. Machine Learning
  2. Natural Language Processing

Key Takeaways

Automatically generating intelligent, context-aware tags with the help of built-on taxonomy and a rule-based trained model will relieve the burden of content creators and publishers substantially. Besides, it will also aid in improving SEO rankings by organizing content more effectively and making it easily searchable.

See how your enterprise can leverage this solution. Contact our experts at business@srijan.net

Subscribe to our newsletter