Noise Detection for Distant Supervised Named Entity Recognition

Wang Jiacheng; Wang Kai; Wang Haofen; Du Wen; He Zhidong; Ruan Tong; Liu Jingping

doi:10.7544/issn1000-1239.202220999

Journal of Computer Research and Development > 2024 > 61(4): 916-928. > DOI: 10.7544/issn1000-1239.202220999

Wang Jiacheng, Wang Kai, Wang Haofen, Du Wen, He Zhidong, Ruan Tong, Liu Jingping. Noise Detection for Distant Supervised Named Entity Recognition[J]. Journal of Computer Research and Development, 2024, 61(4): 916-928. DOI: 10.7544/issn1000-1239.202220999

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Noise Detection for Distant Supervised Named Entity Recognition

1.
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237
2.
College of Design and Innovation, Tongji University, Shanghai 200092
3.
DS Information Technology Co., Ltd., Shanghai 200032

Funds: This work was supported by the Shanghai Municipal Special Fund for Promoting High-quality Development of Industries (2021-GZL-RGZN-01018), the National Key Research and Development Program of China (2021YFC2701800, 2021YFC2701801), the Open Project of Zhejiang Lab (2019ND0AB01), and the Shanghai Sailing Program (23YF1409400).

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Author Bio:
Wang Jiacheng: born in 2000. Master candidate. His main research interests include machine learning and information extraction

Wang Kai: born in 1997. Master candidate. His main research interests include machine learning and data mining

Wang Haofen: born in 1982. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include knowledge graph, natural language processing, and data mining

Du Wen: born in 1975. Professoriate senior engineer. His main research interests include big data and artificial intelligence

He Zhidong: born in 1989. PhD, senior engineer. His main research interests include complex networks and knowledge graph

Ruan Tong: born in 1973. PhD, professor, PhD supervisor. Member of CCF. Her main research interests include knowledge graph, data mining, and data quality assessment

Liu Jingping: born in 1991. PhD, lecturer. His main research interests include knowledge graph and natural language processing
Received Date: December 06, 2022
Revised Date: June 29, 2023
Available Online: January 29, 2024

Graphical Abstract

Abstract

Abstract

On distantly supervised named entity recognition (NER), there are many reinforcement learning based approaches, which exploit the powerful decision-making ability of reinforcement learning to detect noise from the automatically labeled data generated by distant supervision. However, the structures of the policy network models used are typically simple, which results in a weak ability to recognize noisy instances. Furthermore, correct instances are identified at sentence level, resulting in part of the useful information in the sentence being discarded. In this paper, we propose a new reinforcement learning based method for distantly supervised NER, named RLTL-DSNER, which can detect correct instances at token level from noisy data generated by distant supervision, proposing to reduce the negative impact of noisy instances on distantly supervised NER model. Specifically, we introduce a tag confidence function to identify correct instances accurately. In addition, we propose a novel pretraining strategy for the NER model. This strategy can provide accurate state representations and effective reward values for the initial training of the reinforcement learning model. The pre-training strategy can help guide it to update in the right direction. We conduct experiments on four datasets to verify the superiority of the RLTL-DSNER method, gaining 4.28% F1 improvement on NEWS dataset over state-of-the-art methods.
- named entity recognition,
- distant supervision,
- deep reinforcement learning,
- noise detection,
- pre-training strategy

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References (38)

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