@inproceedings{li-etal-2021-fitannotator,
title = "{FITA}nnotator: A Flexible and Intelligent Text Annotation System",
author = "Li, Yanzeng and
Yu, Bowen and
Quangang, Li and
Liu, Tingwen",
editor = "Sil, Avi and
Lin, Xi Victoria",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-demos.5",
doi = "10.18653/v1/2021.naacl-demos.5",
pages = "35--41",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T FITAnnotator: A Flexible and Intelligent Text Annotation System
%A Li, Yanzeng
%A Yu, Bowen
%A Quangang, Li
%A Liu, Tingwen
%Y Sil, Avi
%Y Lin, Xi Victoria
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-fitannotator
%X 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.
%R 10.18653/v1/2021.naacl-demos.5
%U https://aclanthology.org/2021.naacl-demos.5
%U https://doi.org/10.18653/v1/2021.naacl-demos.5
%P 35-41
Markdown (Informal)
[FITAnnotator: A Flexible and Intelligent Text Annotation System](https://aclanthology.org/2021.naacl-demos.5) (Li et al., NAACL 2021)
ACL
- Yanzeng Li, Bowen Yu, Li Quangang, and Tingwen Liu. 2021. FITAnnotator: A Flexible and Intelligent Text Annotation System. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, pages 35–41, Online. Association for Computational Linguistics.