Lasertag is a prototype for a tool that aims to reanimate the newsroom archive, providing journalists with the ability to quickly add context to their stories using archival content.
A product of Al Jazeera Canvas’s Media in Context hackathon in Doha, Qatar, Lasertag suggests related archival stories on two axes. First, it uses named entity recognition to suggest inline links to related stories. Second, it offers article-wide suggestions of additional content to embed. Christopher Wink suggests that it can “speed contextual, internal, in-story linking (and alleviate any loss of institutional memory),” while Mathew Ingram says of Lasertag that “the need for links is number one…hyperlinking is the lifeblood of the web.”
Lasertag was built on Django with the help of several natural language libraries and services, including Python’s nltk and OpenCalais. View the project on Github.
