@inproceedings{prieur-etal-2023-k,
title = "K-pop and fake facts: from texts to smart alerting for maritime security",
author = "Prieur, Maxime and
Gahbiche, Souhir and
Gadek, Guillaume and
Gatepaille, Sylvain and
Vasnier, Kilian and
Justine, Valerian",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.49",
doi = "10.18653/v1/2023.acl-industry.49",
pages = "510--517",
abstract = "Maritime security requires full-time monitoring of the situation, mainly based on technical data (radar, AIS) but also from OSINT-like inputs (e.g., newspapers). Some threats to the operational reliability of this maritime surveillance, such as malicious actors, introduce discrepancies between hard and soft data (sensors and texts), either by tweaking their AIS emitters or by emitting false information on pseudo-newspapers. Many techniques exist to identify these pieces of false information, including using knowledge base population techniques to build a structured view of the information. This paper presents a use case for suspect data identification in a maritime setting. The proposed system UMBAR ingests data from sensors and texts, processing them through an information extraction step, in order to feed a Knowledge Base and finally perform coherence checks between the extracted facts.",
}
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%0 Conference Proceedings
%T K-pop and fake facts: from texts to smart alerting for maritime security
%A Prieur, Maxime
%A Gahbiche, Souhir
%A Gadek, Guillaume
%A Gatepaille, Sylvain
%A Vasnier, Kilian
%A Justine, Valerian
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F prieur-etal-2023-k
%X Maritime security requires full-time monitoring of the situation, mainly based on technical data (radar, AIS) but also from OSINT-like inputs (e.g., newspapers). Some threats to the operational reliability of this maritime surveillance, such as malicious actors, introduce discrepancies between hard and soft data (sensors and texts), either by tweaking their AIS emitters or by emitting false information on pseudo-newspapers. Many techniques exist to identify these pieces of false information, including using knowledge base population techniques to build a structured view of the information. This paper presents a use case for suspect data identification in a maritime setting. The proposed system UMBAR ingests data from sensors and texts, processing them through an information extraction step, in order to feed a Knowledge Base and finally perform coherence checks between the extracted facts.
%R 10.18653/v1/2023.acl-industry.49
%U https://aclanthology.org/2023.acl-industry.49
%U https://doi.org/10.18653/v1/2023.acl-industry.49
%P 510-517
Markdown (Informal)
[K-pop and fake facts: from texts to smart alerting for maritime security](https://aclanthology.org/2023.acl-industry.49) (Prieur et al., ACL 2023)
ACL
- Maxime Prieur, Souhir Gahbiche, Guillaume Gadek, Sylvain Gatepaille, Kilian Vasnier, and Valerian Justine. 2023. K-pop and fake facts: from texts to smart alerting for maritime security. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 510–517, Toronto, Canada. Association for Computational Linguistics.