An Empirical Cross Domain-Specific Entity Recognition with Domain Vector-Reference-Cited by-同舟云学术

An Empirical Cross Domain-Specific Entity Recognition with Domain Vector

Published:2022-10-17 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Chen Wei¹,Han Songqiao¹,Huang Hailiang²

Affiliation:

1. Shanghai University of Finance and Economics, Shanghai, China

2. Shanghai University of Finance and Economics & Shanghai FlagInfo Information Incorporated Technology Co., Ltd, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557545

Reference17 articles.

1. Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

2. A pre-training and self-training approach for biomedical named entity recognition

3. Building a comprehensive syntactic and semantic corpus of Chinese clinical texts

4. Multi-Cell Compositional LSTM for NER Domain Adaptation

5. Shaohua Jiang , Shan Zhao , Kai Hou , Yang Liu , Li Zhang , 2019 . A BERTBiLSTM-CRF model for Chinese electronic medical records named entity recognition . In 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 166--169 . Shaohua Jiang, Shan Zhao, Kai Hou, Yang Liu, Li Zhang, et al. 2019. A BERTBiLSTM-CRF model for Chinese electronic medical records named entity recognition. In 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 166--169.

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