@inproceedings{xia-etal-2021-lome,
title = "{LOME}: Large Ontology Multilingual Extraction",
author = "Xia, Patrick and
Qin, Guanghui and
Vashishtha, Siddharth and
Chen, Yunmo and
Chen, Tongfei and
May, Chandler and
Harman, Craig and
Rawlins, Kyle and
White, Aaron Steven and
Van Durme, Benjamin",
editor = "Gkatzia, Dimitra and
Seddah, Djam{\'e}",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.19",
doi = "10.18653/v1/2021.eacl-demos.19",
pages = "149--159",
abstract = "We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.",
}
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<abstract>We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.</abstract>
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%0 Conference Proceedings
%T LOME: Large Ontology Multilingual Extraction
%A Xia, Patrick
%A Qin, Guanghui
%A Vashishtha, Siddharth
%A Chen, Yunmo
%A Chen, Tongfei
%A May, Chandler
%A Harman, Craig
%A Rawlins, Kyle
%A White, Aaron Steven
%A Van Durme, Benjamin
%Y Gkatzia, Dimitra
%Y Seddah, Djamé
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F xia-etal-2021-lome
%X We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.
%R 10.18653/v1/2021.eacl-demos.19
%U https://aclanthology.org/2021.eacl-demos.19
%U https://doi.org/10.18653/v1/2021.eacl-demos.19
%P 149-159
Markdown (Informal)
[LOME: Large Ontology Multilingual Extraction](https://aclanthology.org/2021.eacl-demos.19) (Xia et al., EACL 2021)
ACL
- Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, and Benjamin Van Durme. 2021. LOME: Large Ontology Multilingual Extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 149–159, Online. Association for Computational Linguistics.