Zhen Gan


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CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities
Jia Fu | Zhen Gan | Zhucong Li | Sirui Li | Dianbo Sui | Yubo Chen | Kang Liu | Jun Zhao
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition,Track 9 - Chinese. In this task, we need to identify the entity boundaries and categorylabels for the six identified categories of CW,LOC, PER, GRP, CORP, and PORD.The task focuses on detecting semantically ambiguous and complex entities in short and low-context settings. We constructed a hybrid system based on Roberta-large model with three training mechanisms and a series of data gugmentation.Three training mechanisms include adversarial training, Child-Tuning training, and continued pre-training. The core idea of the hybrid system is to improve the performance of the model in complex environments by introducing more domain knowledge through data augmentation and continuing pre-training domain adaptation of the model. Our proposed method in this paper achieves a macro-F1 of 0.797 on the final test set, ranking second.

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CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
Jia Fu | Sirui Li | Hui Ming Yuan | Zhucong Li | Zhen Gan | Yubo Chen | Kang Liu | Jun Zhao | Shengping Liu
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This paper presents a description of our system in SMM4H-2022, where we participated in task 1a,task 4, and task 6 to task 10. There are three main challenges in SMM4H-2022, namely the domain shift problem, the prediction bias due to category imbalance, and the noise in informal text. In this paper, we propose a unified framework for the classification and named entity recognition tasks to solve the challenges, and it can be applied to both English and Spanish scenarios. The results of our system are higher than the median F1-scores for 7 tasks and significantly exceed the F1-scores for 6 tasks. The experimental results demonstrate the effectiveness of our system.


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Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks
Tong Zhou | Zhucong Li | Zhen Gan | Baoli Zhang | Yubo Chen | Kun Niu | Jing Wan | Kang Liu | Jun Zhao | Yafei Shi | Weifeng Chong | Shengping Liu
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. Targeting on deal with two shared challenges, the colloquial text and the imbalance annotation, among those tasks, we apply a customized pre-trained language model and propose various training strategies. Experimental results show the effectiveness of our system. Moreover, we got an F1-score of 0.87 in task 8, which is the highest among all participates.

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CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset
Baoli Zhang | Zhucong Li | Zhen Gan | Yubo Chen | Jing Wan | Kang Liu | Jun Zhao | Shengping Liu | Yafei Shi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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.