CULP
发表于 2025-3-23 10:57:53
ERNIE-AT-CEL: A Chinese Few-Shot Emerging Entity Linking Model Based on ERNIE and Adversarial Traini6 (Chinese Few-shot and Zero-shot Entity Linking). Experimental results demonstrate that the model has high predictive performance and robustness in the Chinese few-shot emerging entity linking task, providing reference and inspiration for research and practice in related fields.
丛林
发表于 2025-3-23 16:54:11
Enhanced CGSN System for Machine Reading Comprehensionl information in scientific texts, as well as the evidence memory network to further alleviate the redundancy issues by saving the selected result in the previous steps. Experiments show that our proposed model performs well on datasets released by NLPCC 2023, and our approach ranks 1. for SMRC Task 2 according to the official results.
感情脆弱
发表于 2025-3-23 21:39:13
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organism
发表于 2025-3-24 01:45:06
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暴行
发表于 2025-3-24 04:53:45
Fantastic Gradients and Where to Find Them: Improving Multi-attribute Text Style Transfer by Quadratethod can effectively improve the multi-attribute-transfer accuracy and quality without compromising single attribute performance. Moreover, our method can be readily integrated with pre-trained auto-encoders, providing an effective and scalable solution for multi-attribute scenarios.
树木心
发表于 2025-3-24 09:00:58
A Study on the Classification of Chinese Medicine Records Using BERT, Chest Impediment as an Examplestopwords removing of TCM cases, which improved 0.087 compared with no TCM stopwords removing. This paper introduces natural language processing into the TCM auxiliary diagnosis problem, in order to improve the informationization, standardization and intelligence of TCM in the new era.
材料等
发表于 2025-3-24 13:09:21
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Talkative
发表于 2025-3-24 15:30:39
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创作
发表于 2025-3-24 20:31:50
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CARK
发表于 2025-3-24 23:40:39
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