@inproceedings{adjali-etal-2020-building,
title = "Building a Multimodal Entity Linking Dataset From Tweets",
author = "Adjali, Omar and
Besan{\c{c}}on, Romaric and
Ferret, Olivier and
Le Borgne, Herv{\'e} and
Grau, Brigitte",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.528",
pages = "4285--4292",
abstract = "The task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as relation extraction or event detection. Most of the studies that address this problem rely only on textual documents while an increasing number of sources are multimedia, in particular in the context of social media where messages are often illustrated with images. In this article, we address the Multimodal Entity Linking (MEL) task, and more particularly the problem of its evaluation. To this end, we propose a novel method to quasi-automatically build annotated datasets to evaluate methods on the MEL task. The method collects text and images to jointly build a corpus of tweets with ambiguous mentions along with a Twitter KB defining the entities. We release a new annotated dataset of Twitter posts associated with images. We study the key characteristics of the proposed dataset and evaluate the performance of several MEL approaches on it.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>The task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as relation extraction or event detection. Most of the studies that address this problem rely only on textual documents while an increasing number of sources are multimedia, in particular in the context of social media where messages are often illustrated with images. In this article, we address the Multimodal Entity Linking (MEL) task, and more particularly the problem of its evaluation. To this end, we propose a novel method to quasi-automatically build annotated datasets to evaluate methods on the MEL task. The method collects text and images to jointly build a corpus of tweets with ambiguous mentions along with a Twitter KB defining the entities. We release a new annotated dataset of Twitter posts associated with images. We study the key characteristics of the proposed dataset and evaluate the performance of several MEL approaches on it.</abstract>
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%0 Conference Proceedings
%T Building a Multimodal Entity Linking Dataset From Tweets
%A Adjali, Omar
%A Besançon, Romaric
%A Ferret, Olivier
%A Le Borgne, Hervé
%A Grau, Brigitte
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F adjali-etal-2020-building
%X The task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as relation extraction or event detection. Most of the studies that address this problem rely only on textual documents while an increasing number of sources are multimedia, in particular in the context of social media where messages are often illustrated with images. In this article, we address the Multimodal Entity Linking (MEL) task, and more particularly the problem of its evaluation. To this end, we propose a novel method to quasi-automatically build annotated datasets to evaluate methods on the MEL task. The method collects text and images to jointly build a corpus of tweets with ambiguous mentions along with a Twitter KB defining the entities. We release a new annotated dataset of Twitter posts associated with images. We study the key characteristics of the proposed dataset and evaluate the performance of several MEL approaches on it.
%U https://aclanthology.org/2020.lrec-1.528
%P 4285-4292
Markdown (Informal)
[Building a Multimodal Entity Linking Dataset From Tweets](https://aclanthology.org/2020.lrec-1.528) (Adjali et al., LREC 2020)
ACL
- Omar Adjali, Romaric Besançon, Olivier Ferret, Hervé Le Borgne, and Brigitte Grau. 2020. Building a Multimodal Entity Linking Dataset From Tweets. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4285–4292, Marseille, France. European Language Resources Association.