Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume -, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2023.3252084
Keywords
Estimation; Optimization; Training; Dictionaries; Learning systems; Labeling; Electronic mail; Class imbalance; class prior estimation; named entity recognition (NER); positive unlabeled (PU) learning
Ask authors/readers for more resources
This article proposes a novel PU learning method for distantly supervised NER, which can automatically handle class imbalance and does not rely on class prior estimation, resulting in state-of-the-art performance.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available