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DC Field | Value | Language |
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dc.date.accessioned | 2020-03-24T11:50:07Z | - |
dc.date.available | 2020-03-24T11:50:07Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Mamo, N. (2019). ELD : Event TimeLine Detection : participant-based approach to tracking events (Master's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/52983 | - |
dc.description | M.SC.ARTIFICIAL INTELLIGENCE | en_GB |
dc.description.abstract | People all over the world watch and talk as events unfold, but what does it take for a machine to truly track an event through this crowd-sourced narration? Event TimeLine Detection (ELD) is a real-time Topic Detection and Tracking (TDT) solution to track events using Twitter with the hypothesis that it takes a deeper understanding of the event’s domain for a machine to describe its evolution thoroughly. We test this hypothesis predominantly on football matches as we propose the novel concept of Automatic Participant Detection (APD) to extract an event’s participants before it even starts. We use the detected participants to track events in the TDT component, in which we contribute a novel feature-pivot algorithm. Seeking to explain developments within events, TDT hands our two novel summarization algorithms not only the corpus they are meant to describe, but also the topical keywords within. Our evaluation demonstrates APD’s potential to discover participants early on while exposing the algorithm’s dependency on well-defined environments. As a result, TDT boosts its event coverage through the newly-found participants. TDT’s understanding of developments and their make-up also leads to improved expressiveness in summaries when compared to authoritative content from Twitter and the mainstream media. In fact, an interview with Paul Doyle, a football writer at The Guardian, reveals that ELD’s improvements shift the focus of event tracking from TDT to summarization. Through ELD and its contributions we show that although machines may not have the same understanding that humans accrue over time, they can gain it. In turn, this comprehension permits them to track events closely. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Social networks | en_GB |
dc.subject | Data sets | en_GB |
dc.subject | Information retrieval | en_GB |
dc.title | ELD : Event TimeLine Detection : participant-based approach to tracking events | en_GB |
dc.type | masterThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Artificial Intelligence | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Mamo, Nicholas | - |
Appears in Collections: | Dissertations - FacICT - 2019 Dissertations - FacICTAI - 2019 |
Files in This Item:
File | Description | Size | Format | |
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19MAIPT010.pdf | 2 MB | Adobe PDF | View/Open |
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