Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing
By Michael Stewart, Wei Liu, R. Cardell-Oliver. 2019
23 citations recorded by the source corpus.
We introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing. As an annotation tool, Redcoat also facilitates knowledge elicitation by allowing the creation and continuous refinement of concept hierarchies during annotation. It aims to minimise not only annotation time but the time it takes for project creators to set up and distribute projects to annotators. Projects created using the web-based interface can be rapidly distributed to a list of email addresses. Redcoat handles the propagation of documents amongst annotators and automatically scales the annotation workload depending on the number of active annotators. In this paper we discuss these key features and outline Redcoat’s system architecture. We also highlight Redcoat’s unique benefits over existing annotation tools via a qualitative comparison.
Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing, Information Extraction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alexander M. Rush
- Colin Raffel
- Victor Sanh
- Thibault Févry
- Mohit Bansal
- Luke S. Zettlemoyer
- Canwen Xu
- Albert Webson
In Management Science and Operations Research
- Colin Raffel
- Alexander M. Rush
- Stephen H. Bach
- Jason Alan Fries
- Zhiqing Sun
- Victor Sanh
- Canwen Xu
- Thibault Févry
In Natural Language Processing
- Colin Raffel
- Alexander M. Rush
- Thibault Févry
- Victor Sanh
- Zhiqing Sun
- Shanya Sharma Sharma
- Han Wang
- Manan Dey
In Information Extraction
Citations in the corpus
- PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022, background): “Other systems support collaboration among multiple annotators (Yang et al., 2018; Stewart et al., 2019).”