@inproceedings{cardoso-cohn-2022-multimodal,
title = "The Multimodal Annotation Software Tool ({MAST})",
author = "Cardoso, Bruno and
Cohn, Neil",
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
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.736",
pages = "6822--6828",
abstract = "Multimodal combinations of writing and pictures have become ubiquitous in contemporary society, and scholars have increasingly been turning to analyzing these media. Here we present a resource for annotating these complex documents: the Multimodal Annotation Software Tool (MAST). MAST is an application that allows users to analyze visual and multimodal documents by selecting and annotating visual regions, and to establish relations between annotations that create dependencies and/or constituent structures. By means of schema publications, MAST allows annotation theories to be citable, while evolving and being shared. Documents can be annotated using multiple schemas simultaneously, offering more comprehensive perspectives. As a distributed, client-server system MAST allows for collaborative annotations across teams of users, and features team management and resource access functionalities, facilitating the potential for implementing open science practices. Altogether, we aim for MAST to provide a powerful and innovative annotation tool with application across numerous fields engaging with multimodal media.",
}
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%0 Conference Proceedings
%T The Multimodal Annotation Software Tool (MAST)
%A Cardoso, Bruno
%A Cohn, Neil
%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 Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F cardoso-cohn-2022-multimodal
%X Multimodal combinations of writing and pictures have become ubiquitous in contemporary society, and scholars have increasingly been turning to analyzing these media. Here we present a resource for annotating these complex documents: the Multimodal Annotation Software Tool (MAST). MAST is an application that allows users to analyze visual and multimodal documents by selecting and annotating visual regions, and to establish relations between annotations that create dependencies and/or constituent structures. By means of schema publications, MAST allows annotation theories to be citable, while evolving and being shared. Documents can be annotated using multiple schemas simultaneously, offering more comprehensive perspectives. As a distributed, client-server system MAST allows for collaborative annotations across teams of users, and features team management and resource access functionalities, facilitating the potential for implementing open science practices. Altogether, we aim for MAST to provide a powerful and innovative annotation tool with application across numerous fields engaging with multimodal media.
%U https://aclanthology.org/2022.lrec-1.736
%P 6822-6828
Markdown (Informal)
[The Multimodal Annotation Software Tool (MAST)](https://aclanthology.org/2022.lrec-1.736) (Cardoso & Cohn, LREC 2022)
ACL
- Bruno Cardoso and Neil Cohn. 2022. The Multimodal Annotation Software Tool (MAST). In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6822–6828, Marseille, France. European Language Resources Association.