@inproceedings{zhang-etal-2021-croano,
title = "{C}ro{A}no : A Crowd Annotation Platform for Improving Label Consistency of {C}hinese {NER} Dataset",
author = "Zhang, Baoli and
Li, Zhucong and
Gan, Zhen and
Chen, Yubo and
Wan, Jing and
Liu, Kang and
Zhao, Jun and
Liu, Shengping and
Shi, Yafei",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.32",
doi = "10.18653/v1/2021.emnlp-demo.32",
pages = "275--282",
abstract = "In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96{\%} and +2.57{\%} F1 respectively in model performance.",
}
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<abstract>In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96% and +2.57% F1 respectively in model performance.</abstract>
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%0 Conference Proceedings
%T CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset
%A Zhang, Baoli
%A Li, Zhucong
%A Gan, Zhen
%A Chen, Yubo
%A Wan, Jing
%A Liu, Kang
%A Zhao, Jun
%A Liu, Shengping
%A Shi, Yafei
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zhang-etal-2021-croano
%X In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96% and +2.57% F1 respectively in model performance.
%R 10.18653/v1/2021.emnlp-demo.32
%U https://aclanthology.org/2021.emnlp-demo.32
%U https://doi.org/10.18653/v1/2021.emnlp-demo.32
%P 275-282
Markdown (Informal)
[CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset](https://aclanthology.org/2021.emnlp-demo.32) (Zhang et al., EMNLP 2021)
ACL
- Baoli Zhang, Zhucong Li, Zhen Gan, Yubo Chen, Jing Wan, Kang Liu, Jun Zhao, Shengping Liu, and Yafei Shi. 2021. CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 275–282, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.