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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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發(fā)表于 2025-3-21 18:36:43 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Natural Language Processing and Chinese Computing
副標題13th National CCF Co
編輯Derek F. Wong,Zhongyu Wei,Muyun Yang
視頻videohttp://file.papertrans.cn/670/669628/669628.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面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
關(guān)鍵詞Information extraction; Machine translation; Discourse, dialogue and pragmatics; Natural language gener
版次1
doihttps://doi.org/10.1007/978-981-97-9431-7
isbn_softcover978-981-97-9430-0
isbn_ebook978-981-97-9431-7Series 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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https://doi.org/10.1007/978-981-97-9431-7Information extraction; Machine translation; Discourse, dialogue and pragmatics; Natural language gener
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Learning to?Generate Style-Specific Adapters for?Stylized Dialogue Generatione systems. However, a major challenge to this task is the paucity of supervised data, resulting in suboptimal performance. Although some unsupervised methods have emerged, they tend to handle only a limited range of dialogue styles simultaneously. Retraining becomes necessary when new dialogue style
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Multi-hop Reading Comprehension Model Based on?Abstract Meaning Representation and?Multi-task Joint ing supporting facts from multiple paragraphs in the document and reasoning to get the answer. But some problems have not been well solved: during the process of extracting the answer, it is often disturbed by the non-real answer (i.e. similar answer) in the document; the lack of a related expressio
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Leveraging Large Language Models for?QA Dialogue Dataset Construction and?Analysis in?Public ServiceI) context. Existing datasets often lack the necessary interactive features for effective and efficient interactions, hindering the development of customized and emotionally responsive systems. As public service demands become more diverse and complex in HRI, traditional datasets fail to support hig
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