@inproceedings{bikaun-etal-2022-quickgraph,
title = "{Q}uick{G}raph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text",
author = "Bikaun, Tyler and
Stewart, Michael and
Liu, Wei",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.27/",
doi = "10.18653/v1/2022.acl-demo.27",
pages = "270--278",
abstract = "Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. Adoption of unsupervised techniques for automated annotation have thus become popular. However, these techniques rely heavily on dictionaries, gazetteers, and knowledge bases. While such resources are abundant for general domains, they are scarce for specialised technical domains. To tackle this challenge, we present QuickGraph, the first collaborative MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. QuickGraph`s main contribution is a set of novel features that enable knowledge graph extraction through rapid and consistent complex multi-task entity and relation annotation. In this paper, we discuss these key features and qualitatively compare QuickGraph to existing annotation tools."
}
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<abstract>Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. Adoption of unsupervised techniques for automated annotation have thus become popular. However, these techniques rely heavily on dictionaries, gazetteers, and knowledge bases. While such resources are abundant for general domains, they are scarce for specialised technical domains. To tackle this challenge, we present QuickGraph, the first collaborative MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. QuickGraph‘s main contribution is a set of novel features that enable knowledge graph extraction through rapid and consistent complex multi-task entity and relation annotation. In this paper, we discuss these key features and qualitatively compare QuickGraph to existing annotation tools.</abstract>
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%0 Conference Proceedings
%T QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text
%A Bikaun, Tyler
%A Stewart, Michael
%A Liu, Wei
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F bikaun-etal-2022-quickgraph
%X Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators. Adoption of unsupervised techniques for automated annotation have thus become popular. However, these techniques rely heavily on dictionaries, gazetteers, and knowledge bases. While such resources are abundant for general domains, they are scarce for specialised technical domains. To tackle this challenge, we present QuickGraph, the first collaborative MT-IE annotation tool built with indirect weak supervision and clustering to maximise annotator productivity. QuickGraph‘s main contribution is a set of novel features that enable knowledge graph extraction through rapid and consistent complex multi-task entity and relation annotation. In this paper, we discuss these key features and qualitatively compare QuickGraph to existing annotation tools.
%R 10.18653/v1/2022.acl-demo.27
%U https://aclanthology.org/2022.acl-demo.27/
%U https://doi.org/10.18653/v1/2022.acl-demo.27
%P 270-278
Markdown (Informal)
[QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text](https://aclanthology.org/2022.acl-demo.27/) (Bikaun et al., ACL 2022)
ACL