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Titlebook: Natural Language Processing and Chinese Computing; 13th National CCF Co Derek F. Wong,Zhongyu Wei,Muyun Yang Conference proceedings 2025 Th

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发表于 2025-3-21 16:33:59 | 显示全部楼层 |阅读模式
书目名称Natural Language Processing and Chinese Computing
副标题13th National CCF Co
编辑Derek F. Wong,Zhongyu Wei,Muyun Yang
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Natural Language Processing and Chinese Computing; 13th National CCF Co Derek F. Wong,Zhongyu Wei,Muyun Yang Conference proceedings 2025 Th
描述.The five-volume set LNCS 15359 - 15363 constitutes the refereed proceedings of the 13th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024..The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Extraction and Knowledge Graph; Information Retrieval, Dialogue Systems, and Question Answering; Large Language Models and Agents; Machine Learning for NLP; Machine Translation and Multilinguality; Multi-modality and Explainability; NLP Applications and Text Mining; Sentiment Analysis, Argumentation Mining, and Social Media; Summarization and Generation..
出版日期Conference proceedings 2025
关键词Information extraction; Machine translation; Discourse, dialogue and pragmatics; Natural language gener
版次1
doihttps://doi.org/10.1007/978-981-97-9434-8
isbn_softcover978-981-97-9433-1
isbn_ebook978-981-97-9434-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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书目名称Natural Language Processing and Chinese Computing年度引用学科排名




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书目名称Natural Language Processing and Chinese Computing读者反馈学科排名




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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.
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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.
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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.
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