In New Release, Atex Fully Integrates Polopoly, Text Mining

By: E&P Staff Atex has released Polopoly 9.13, its latest version of the Atex Web content management system which now fully integrates with the Atex Text Mining engine to automatically tag, categorize and enrich content for increased pages views and time on site. A new widget allows content to be batch categorized, further enhancing visitors' search experience while providing internal users with a built-in discovery and knowledge-management tool.

With the new release and Text Mining module, editors can streamline content classification. Instead of applying relevant categories manually, the text-mining engine does it automatically. By simply clicking a button, editors instruct the engine to analyze a piece of content and suggest relevant categories. Suggestions are based on the metadata and IPTC categorization, as well as such specifics as person, company, organization, and location. Text Mining integration is based on a Web services interface designed for consistent, efficient classification.

"Atex Text Mining makes it possible to create a more valuable experience for Web site visitors by serving up highly relevant news content, combined with meaningful local and national advertising content," Atex Head of Global Product Management Peter Marsh said in a statement. Under Atex's Digital News and Advertising framework, he added, "media companies can respond to the personalized requirements of individual users, delivering targeted news and ad content to one or more digital channels."

Besides helping to organize media companies' great volumes of information, "this new release also improves the probability that content will be found by search engines, which further increase traffic and Web site access," said Atex Global Product Manager of Polopoly Anders Weijnitz. Content enrichment also can improve editorial productivity and promote content consistency across newsrooms, according to Atex.

Polopoly 9.13 automatically places classified content in dynamic lists based on metadata selections in the repository. Lists can automatically serve up older stories using related-content links placed in context alongside the current articles, allowing interested users to "read more" or "find similar" stories based on information mined from the articles they are viewing. Links to related content help generate new revenue by increasing the average clicks per.

Publishers can create new pages on the fly based entirely on re-use of archived content that's been categorized by metadata, which helps cut costs associated with creating new products.