纪念性
发表于 2025-3-21 16:33:59
书目名称Natural Language Processing and Chinese Computing影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0669624<br><br> <br><br>书目名称Natural Language Processing and Chinese Computing读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0669624<br><br> <br><br>
装入胶囊
发表于 2025-3-21 22:23:57
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吞噬
发表于 2025-3-22 03:38:33
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疾驰
发表于 2025-3-22 06:21:28
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BILIO
发表于 2025-3-22 10:21:19
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前面
发表于 2025-3-22 14:11:55
An Iterative Framework for Document-Level Event Argument Extraction Assisted by Long Short-Term Memoteration, the extracted arguments, considered short-term information, are used to enrich a dynamic prompt for extraction. Experiments on the news dataset DocEE demonstrate that our model outperforms previous methods. The ablation study also proves the effectiveness of each module.
Physiatrist
发表于 2025-3-22 17:13:04
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杀菌剂
发表于 2025-3-23 00:02:49
EDNER: Edge Detection for Named Entity Recognitiononal layers and prediction layers extract entity spans by detecting entity edges. Experimental results show that our method outperforms existing state-of-the-art methods and achieves highest F1 scores on four benchmark NER datasets.
投射
发表于 2025-3-23 04:58:53
VPformer: Multivariate Time Series Forecasting with Variable Correlation and Triple Patch Correlatioions. A fusion strategy integrates features, providing a holistic view that captures both variable and temporal information. VPformer’s performance is validated on real-world datasets, demonstrating superior forecasting accuracy and computational efficiency.
FEAT
发表于 2025-3-23 06:12:43
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