Events have been a big part of my life since 2016. But no, not those kind of events.
My name is Nicholas Mamo and I’m not a party animal nor particularly outgoing—I’m a doctoral student at the University of Malta. 2016 is the year when I started working on my undergraduate dissertation and the subject was events.
More specifically, the research area I chose for my undergraduate dissertation was Topic Detection and Tracking: a subset of Artificial Intelligence that builds timelines for events, like this one. That sounds simple, but it’s not.
To generate a timeline like that one, Topic Detection and Tracking systems need to be able to, at least:
- Detect when something happened,
- Track it until it ends, and
- Describe what happened.
And that’s not even all of it. Our way of programming machines to understand events is changing, which allows us create timelines that describe the event better.
A lot has changed since 2016; I graduated, obtained my Masters degree and started my doctorate, but the one thing that has remained constant is my research area: Topic Detection and Tracking.
Although my supervisors and I have made some great strides, the area of Topic Detection and Tracking remains fairly unknown. Seriously, look it up on Wikipedia—there’s nothing there! This blog’s purpose is to start changing that.
I want to use this blog to explain the basics of Topic Detection and Tracking, to describe the findings and challenges, and to give a glimpse of my research—all in plain English. May you come to appreciate these kind of events as much as I do.