CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet

Zhuoran Jin, Yubo Chen, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Jun Zhao


Abstract
CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. In this paper, we propose an information extraction toolkit, called CogIE, which is a bridge connecting raw texts and CogNet. CogIE has three features: versatile, knowledge-grounded and extensible. First, CogIE is a versatile toolkit with a rich set of functional modules, including named entity recognition, entity typing, entity linking, relation extraction, event extraction and frame-semantic parsing. Second, as a knowledge-grounded toolkit, CogIE can ground the extracted facts to CogNet and leverage different types of knowledge to enrich extracted results. Third, for extensibility, owing to the design of three-tier architecture, CogIE is not only a plug-and-play toolkit for developers but also an extensible programming framework for researchers. We release an open-access online system to visually extract information from texts. Source code, datasets and pre-trained models are publicly available at GitHub, with a short instruction video.
Anthology ID:
2021.acl-demo.11
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
92–98
Language:
URL:
https://aclanthology.org/2021.acl-demo.11
DOI:
10.18653/v1/2021.acl-demo.11
Bibkey:
Cite (ACL):
Zhuoran Jin, Yubo Chen, Dianbo Sui, Chenhao Wang, Zhipeng Xue, and Jun Zhao. 2021. CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 92–98, Online. Association for Computational Linguistics.
Cite (Informal):
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (Jin et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.11.pdf
Code
 jinzhuoran/cogie
Data
FrameNet