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Titlebook: Information Retrieval; 26th China Conferenc Zhicheng Dou,Qiguang Miao,Guang Jia Conference proceedings 2020 Springer Nature Switzerland AG

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發(fā)表于 2025-3-25 04:32:21 | 只看該作者
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發(fā)表于 2025-3-25 10:31:39 | 只看該作者
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發(fā)表于 2025-3-25 13:56:46 | 只看該作者
Semi-supervised Sentiment Analysis for Chinese Stock Texts in Scarce Labeled Data Scenario and Priceas most of research are conducted in English, data sources and labeled data are inadequate in Chinese. Especially for natural language processing tasks in specific domain where specialized labeled data are required to train models to adapt to terminology processing, specialized labeled Chinese in te
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發(fā)表于 2025-3-25 18:39:21 | 只看該作者
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發(fā)表于 2025-3-25 20:33:21 | 只看該作者
Investigating Fine-Grained Usefulness Perception Process in Mobile Searchvior and propose a user model for the fine-grained usefulness perception process in mobile search. Our research sheds light on improving user models in mobile search evaluation metrics and other mobile search-related applications.
26#
發(fā)表于 2025-3-26 01:15:39 | 只看該作者
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發(fā)表于 2025-3-26 08:14:20 | 只看該作者
A Framework for Identifying Event’s Relevance Comments in Twitterrelevance comment in twitter. Before the matching module, we adopted the Rake algorithm to extract key phrases from the event’s news and then the key phrases were used for twitter search to construct twitter relevant comments dataset. Based on this dataset, the effectiveness of different text matchi
28#
發(fā)表于 2025-3-26 12:26:24 | 只看該作者
Enriching Pre-trained Language Model with Dependency Syntactic Information for Chemical-Protein Inte context representation. Finally, the sequence information and syntactic information are fused and mapped to the category feature space through a fully-connected layer. The evaluation on the original ChemProt corpus demonstrates that in comparison to other pre-trained model-based methods, our method
29#
發(fā)表于 2025-3-26 16:42:45 | 只看該作者
30#
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