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Titlebook: Web Information Systems Engineering – WISE 2023; 24th International C Feng Zhang,Hua Wang,Rui Zhou Conference proceedings 2023 The Editor(s

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書(shū)目名稱Web Information Systems Engineering – WISE 2023
副標(biāo)題24th International C
編輯Feng Zhang,Hua Wang,Rui Zhou
視頻videohttp://file.papertrans.cn/1022/1021540/1021540.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Web Information Systems Engineering – WISE 2023; 24th International C Feng Zhang,Hua Wang,Rui Zhou Conference proceedings 2023 The Editor(s
描述This book constitutes the proceedings of the 24th International Conference on Web Information Systems Engineering, WISE 2023, held in Melbourne, Victoria, Australia, in October 2023..The 33 full and 40 short papers were carefully reviewed and selected from 137 submissions.?They were organized in topical sections as follows: text and sentiment analysis; question answering and information retrieval; social media and news analysis; security and privacy; web technologies; graph embeddings and link predictions; predictive analysis and machine learning; recommendation systems; natural language processing (NLP) and databases; data analysis and optimization; anomaly and threat detection; streaming data; miscellaneous; explainability and scalability in AI..
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; Text Analysis; Sentiment Analysis; Question Answering System; Information Retri
版次1
doihttps://doi.org/10.1007/978-981-99-7254-8
isbn_softcover978-981-99-7253-1
isbn_ebook978-981-99-7254-8Series 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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Label-Dependent Hypergraph Neural Network for?Enhanced Multi-label Text Classificationodel to capture the semantic information of texts. By integrating text information and combining the hypergraph label structure dependencies for multi-label text classification. Experimental results on three benchmark datasets demonstrate that the LHGN model outperforms state-of-the-art baseline mod
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