TY - GEN
T1 - GAIA
T2 - 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
AU - Li, Manling
AU - Zareian, Alireza
AU - Lin, Ying
AU - Pan, Xiaoman
AU - Whitehead, Spencer
AU - Chen, Brian
AU - Wu, Bo
AU - Ji, Heng
AU - Chang, Shih Fu
AU - Voss, Clare
AU - Napierski, Daniel
AU - Freedman, Marjorie
N1 - Funding Information:
This research is based upon work supported in part by U.S. DARPA AIDA Program No. FA8750-18-2-0014 and KAIROS Program No. FA8750-19-2-1004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Publisher Copyright:
© 2020 Association for Computational Linguistics
PY - 2020
Y1 - 2020
N2 - 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 1, 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 evaluation2.
AB - 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 1, 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 evaluation2.
UR - http://www.scopus.com/inward/record.url?scp=85095293902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095293902&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85095293902
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 77
EP - 86
BT - ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the System Demonstrations
PB - Association for Computational Linguistics (ACL)
Y2 - 5 July 2020 through 10 July 2020
ER -