Class-Imbalanced-Aware Distantly Supervised Named Entity Recognition

Yuren Mao

doi:10.1109/TNNLS.2023.3252084

Class-Imbalanced-Aware Distantly Supervised Named Entity Recognition

2024-09-01

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AI

DOI：10.1109/TNNLS.2023.3252084

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Distantly supervised named entity recognition (NER), which automatically learns NER models without manually labeling data, has gained much attention recently. In distantly supervised NER, positive unlabeled (PU) learning methods have achieved notable success. However, existing PU learning-based NER methods are unable to automatically handle the class imbalance and further depend on the estimation of the unknown class prior; thus, the class imbalance and imperfect estimation of the class prior degenerate the NER performance. To address these issues, this article proposes a novel PU learning method for distantly supervised NER. The proposed method can automatically handle the class imbalance and does not need to engage in class prior estimation, which enables the proposed methods to achieve the state-of-the-art performance. Extensive experiments support our theoretical analysis and validate the superiority of our method.

Keywords:

Estimation

Optimization

Training

Dictionaries

Learning systems

Labeling

Electronic mail

Class imbalance

class prior estimation

named entity recognition (NER)

positive unlabeled (PU) learning

Class-Imbalanced-Aware Distantly Supervised Named Entity Recognition

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