Please use this identifier to cite or link to this item:
Title: From event tracking to event modelling : understanding as a paradigm shift
Authors: Mamo, Nicholas
Layfield, Colin
Azzopardi, Joel
Keywords: RSS feeds
Computer network resources
Data mining
Artificial intelligence
Deep learning (Machine learning)
Machine learning
Issue Date: 2023
Publisher: Springer Cham
Citation: Mamo, N., Layfield, C., Azzopardi, J. (2023). From event tracking to event modelling : understanding as a paradigm shift. 13th International Joint Conference, IC3K 2021. 21-36.
Abstract: In 1998, Topic Detection and Tracking (TDT), or event tracking, was only two years old. By then, however, event tracking’s pioneers had already realized that for algorithms to accomplish their task, to detect and track events in the news media accurately, they had to understand events. Yet it has taken event tracking the decades since then to resolve what it means to understand events and how. Simple interpretations of understanding often failed; the rest never fulfilled the potential of event tracking. Without understanding, event tracking continues to struggle with the same, early problems, which social media aggravated and contemporary applications of events compounded. Therefore in this position paper, we argue that now, more than ever, event tracking needs event understanding. By comparing event tracking’s interpretations of what it means to understand, we demonstrate how early understanding only failed event tracking because it remained too simple in its reliance on linguistics. We adopt a different definition of events, a structured and semantic description that revolves around Who did What, Where and When. Furthermore, we propose how event tracking can fill in the event structure automatically and ahead of time. In the end, we show that understanding remains a complex problem, but event tracking can find in event knowledge a new purpose and its much-awaited paradigm shift.
ISBN: 9783031359231
Appears in Collections:Scholarly Works - FacICTAI

Files in This Item:
File Description SizeFormat 
  Restricted Access
696.67 kBAdobe PDFView/Open Request a copy

Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.