FITAnnotator: A Flexible and Intelligent Text Annotation System
By Yanzeng Li, Yu Bowen, Quangang Li, Tingwen Liu. 2021
13 citations recorded by the source corpus.
In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation. Benefiting from the fully modular architecture design, FITAnnotator provides a systematic solution for the annotation of a variety of natural language processing tasks, including classification, sequence tagging and semantic role annotation, regardless of the language. Three kinds of interfaces are developed to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers, respectively. FITAnnotator also gives intelligent annotations by introducing task-specific assistant to support and guide the annotators based on active learning and incremental learning strategies. This assistant is able to effectively update from the annotator feedbacks and easily handle the incremental labeling scenarios.
Subfields: Artificial Intelligence, Natural Language Processing, Human-Computer Interaction
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 Natural Language Processing
- Colin Raffel
- Alexander M. Rush
- Thibault Févry
- Victor Sanh
- Zhiqing Sun
- Shanya Sharma Sharma
- Han Wang
- Manan Dey
In Human-Computer Interaction
Citations in the corpus
- PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022, methodology): “More recently, many annotation systems have begun to incorporate learned models to improve workflow, using techniques such as ac-S1 tive learning (Lin et al., 2019; Li et al., 2021) and example recommendation (Lee et al., 2020; Kiela et al., 2021).”