loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Antonella Arca 1 ; Salvatore Carta 2 ; Alessandro Giuliani 2 ; Maria Madalina Stanciu 2 and Diego Reforgiato Recupero 2

Affiliations: 1 BuzzMyVideos, London, U.K. ; 2 Department of Mathematics and Computer Science, Univeristy of Cagliari, Cagliari, Italy

Keyword(s): Tag Annotation, Semantic Enrichment, Machine Learning, Google Trends.

Abstract: The technological evolution of modern content sharing applications led to unbridled increase of video content creation and with it multimedia streaming, content sharing and video advertising. Managing huge volumes of video data becomes critical for various applications such as video browsing, retrieval, and recommendation. In such a context, video tagging, the task of assigning meaningful human-friendly words (i.e., tags) to a video, has become an important pillar for both academia and companies alike. Indeed, tags may be able to effectively summarize the content of the video, and, in turn, attract users and advertisers interests. As manual tags are usually noisy, biased and incomplete, many efforts have been recently made in devising automated video tagging approaches. However, video search engines handle a massive amount of natural language queries every second. Therefore, a key aspect in video tagging consists of proposing tags not only related to video contents, but also popular amongst users searches. In this paper, we propose a novel video tagging approach, in which the proposed tags are generated by identifying semantically related popular search queries (i.e., trends). Experiments demonstrate the viability of our proposal. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.203.235.24

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Arca, A.; Carta, S.; Giuliani, A.; Stanciu, M. and Recupero, D. (2020). Automated Tag Enrichment by Semantically Related Trends. In Proceedings of the 16th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-478-7; ISSN 2184-3252, SciTePress, pages 183-193. DOI: 10.5220/0010108701830193

@conference{webist20,
author={Antonella Arca. and Salvatore Carta. and Alessandro Giuliani. and Maria Madalina Stanciu. and Diego Reforgiato Recupero.},
title={Automated Tag Enrichment by Semantically Related Trends},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - WEBIST},
year={2020},
pages={183-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010108701830193},
isbn={978-989-758-478-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - WEBIST
TI - Automated Tag Enrichment by Semantically Related Trends
SN - 978-989-758-478-7
IS - 2184-3252
AU - Arca, A.
AU - Carta, S.
AU - Giuliani, A.
AU - Stanciu, M.
AU - Recupero, D.
PY - 2020
SP - 183
EP - 193
DO - 10.5220/0010108701830193
PB - SciTePress