Titlebook: Knowledge Science, Engineering and Management; 15th International C Gerard Memmi,Baijian Yang,Meikang Qiu Conference proceedings 2022 The E

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GAN-Based Fusion Adversarial Trainingr a wide variety of adversarial samples, and adversarial training is a very effective method against a wide variety of adversarial sample attacks. However, adversarial training tends to improve the accuracy of the adversarial samples while reducing the accuracy of the original samples. Thus, the rob

STERN

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Fuzzy Information Measures Feature Selection Using Descriptive Statistics Dataracting all the possible relations among features to estimate their informative amount well. Fuzzy information measures are powerful solutions that extract the different feature relations without information loss. However, estimating fuzzy information measures consumes high resources such as space a

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Prompt-Based Self-training Framework for?Few-Shot Named Entity Recognitionbeled examples are given for each entity type. Existing works focus on learning deep NER models with self-training for few-shot NER. Self-training may induce incomplete and noisy labels which do not necessarily improve or even deteriorate the model performance. To address this challenge, we propose

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CorefDRE: Coref-Aware Document-Level Relation Extractiontences. The pronouns are ubiquitous in the document, which can provide reasoning clues for Doc-level RE. However, previous works do not take the pronouns into account. In this paper, we propose .-aware .oc-level . based on Graph Inference Network (CorefDRE) to infer relations. CorefDRE first dynamic

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