@inproceedings{li-etal-2020-gaia,
title = "{GAIA}: A Fine-grained Multimedia Knowledge Extraction System",
author = "Li, Manling and
Zareian, Alireza and
Lin, Ying and
Pan, Xiaoman and
Whitehead, Spencer and
Chen, Brian and
Wu, Bo and
Ji, Heng and
Chang, Shih-Fu and
Voss, Clare and
Napierski, Daniel and
Freedman, Marjorie",
editor = "Celikyilmaz, Asli and
Wen, Tsung-Hsien",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-demos.11",
doi = "10.18653/v1/2020.acl-demos.11",
pages = "77--86",
abstract = "We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology. Our system, GAIA, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos. GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. The system is publicly available at GitHub and DockerHub, with a narrated video that documents the system.",
}
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<abstract>We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology. Our system, GAIA, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos. GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. The system is publicly available at GitHub and DockerHub, with a narrated video that documents the system.</abstract>
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%0 Conference Proceedings
%T GAIA: A Fine-grained Multimedia Knowledge Extraction System
%A Li, Manling
%A Zareian, Alireza
%A Lin, Ying
%A Pan, Xiaoman
%A Whitehead, Spencer
%A Chen, Brian
%A Wu, Bo
%A Ji, Heng
%A Chang, Shih-Fu
%A Voss, Clare
%A Napierski, Daniel
%A Freedman, Marjorie
%Y Celikyilmaz, Asli
%Y Wen, Tsung-Hsien
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-gaia
%X We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology. Our system, GAIA, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos. GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. The system is publicly available at GitHub and DockerHub, with a narrated video that documents the system.
%R 10.18653/v1/2020.acl-demos.11
%U https://aclanthology.org/2020.acl-demos.11
%U https://doi.org/10.18653/v1/2020.acl-demos.11
%P 77-86
Markdown (Informal)
[GAIA: A Fine-grained Multimedia Knowledge Extraction System](https://aclanthology.org/2020.acl-demos.11) (Li et al., ACL 2020)
ACL
- Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, and Marjorie Freedman. 2020. GAIA: A Fine-grained Multimedia Knowledge Extraction System. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 77–86, Online. Association for Computational Linguistics.