Automated Tag Enrichment by Semantically Related Trends

Antonella Arca, Salvatore Carta, Alessandro Giuliani, Maria Stanciu, Diego Recupero

2020

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.

Paper Citation

in Harvard Style

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 - Volume 1: WEBIST, ISBN 978-989-758-478-7, pages 183-193. DOI: 10.5220/0010108701830193

in Bibtex Style

@conference{webist20,
author={Antonella Arca and Salvatore Carta and Alessandro Giuliani and Maria Stanciu and Diego Recupero},
title={Automated Tag Enrichment by Semantically Related Trends},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2020},
pages={183-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010108701830193},
isbn={978-989-758-478-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Automated Tag Enrichment by Semantically Related Trends
SN - 978-989-758-478-7
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