Events have been a big part of my life since 2016. My name is Nicholas Mamo and 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. 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.
That’s not all. 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. 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.