Titlebook: Knowledge Science, Engineering and Management; 11th International C Weiru Liu,Fausto Giunchiglia,Bo Yang Conference proceedings 2018 Spring

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書(shū)目名稱Knowledge Science, Engineering and Management影響因子(影響力)




書(shū)目名稱Knowledge Science, Engineering and Management影響因子(影響力)學(xué)科排名




書(shū)目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Knowledge Science, Engineering and Management被引頻次




書(shū)目名稱Knowledge Science, Engineering and Management被引頻次學(xué)科排名




書(shū)目名稱Knowledge Science, Engineering and Management年度引用




書(shū)目名稱Knowledge Science, Engineering and Management年度引用學(xué)科排名




書(shū)目名稱Knowledge Science, Engineering and Management讀者反饋




書(shū)目名稱Knowledge Science, Engineering and Management讀者反饋學(xué)科排名

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Knowledge Science, Engineering and Management978-3-319-99365-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

神圣不可

https://doi.org/10.1007/978-3-319-99365-2artificial intelligence; classification; classification accuracy; data mining; image processing; informat

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A Biomedical Question Answering System Based on SNOMED-CTxibility and accuracy of the system. The experimental results show that the overall performance of the system has reached a high level, which can give 85% of the correct answer and be used as a biomedical question answering system in a real environment.

強(qiáng)制性

WalkToTopics: Inferring Topic Relations from a Feature Learning Perspectives. Moreover, WalkToTopics is a general model that also can work on exploring topic clusters or extracting sentiments, and can be applied to potential applications, such as ideas tracking and opinion summarization. Finally, we conducted two studies for common users and experts which both quantitative

Truculent

拋射物

PLUMP

TCMEF: A TCM Entity Filter Using Less Textaike and HudongBaike (the two largest Chinese encyclopedia websites) as the main data sources. TCMEF gets an F1 score of 0.9275 in classification, which outperforms general word based short text classification algorithms and is close to a Latent Dirichlet Allocation based model (LDA-SVM) using rich

意外