Anagram 发表于 2025-3-21 18:53:25

书目名称Information Retrieval影响因子(影响力)<br>        http://impactfactor.cn/if/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval影响因子(影响力)学科排名<br>        http://impactfactor.cn/ifr/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval网络公开度<br>        http://impactfactor.cn/at/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval网络公开度学科排名<br>        http://impactfactor.cn/atr/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval被引频次<br>        http://impactfactor.cn/tc/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval被引频次学科排名<br>        http://impactfactor.cn/tcr/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval年度引用<br>        http://impactfactor.cn/ii/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval年度引用学科排名<br>        http://impactfactor.cn/iir/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval读者反馈<br>        http://impactfactor.cn/5y/?ISSN=BK0465191<br><br>        <br><br>书目名称Information Retrieval读者反馈学科排名<br>        http://impactfactor.cn/5yr/?ISSN=BK0465191<br><br>        <br><br>

PHAG 发表于 2025-3-21 22:48:21

Joint Attention LSTM Network for Aspect-Level Sentiment Analysisosed, which aspect attention and sentiment attention are combined to construct a joint attention LSTM network. The experimental results on the benchmark datasets show that the proposed method achieves better performance than the current state-of-the-art.

epicondylitis 发表于 2025-3-22 03:38:13

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钢盔 发表于 2025-3-22 07:40:16

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共同确定为确 发表于 2025-3-22 09:08:18

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Living-Will 发表于 2025-3-22 16:03:52

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Breach 发表于 2025-3-22 18:51:20

Conference proceedings 2018018. The 22 full papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections: Information retrieval, collaborative and social computing, natural language processing.

Hectic 发表于 2025-3-22 23:49:26

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Wordlist 发表于 2025-3-23 03:56:53

A Deep Top-K Relevance Matching Model for Ad-hoc Retrieval level matching scores are aggregated with the term gating network to produce the final relevance score. We have tested our model on two representative benchmark datasets. Experimental results show that our model can significantly outperform existing baseline models.

文件夹 发表于 2025-3-23 07:07:06

A Comparison Between Term-Based and Embedding-Based Methods for Initial Retrieval initial retrieval models on three representative retrieval tasks (Web-QA, Ad-hoc retrieval and CQA respectively). The results show that embedding based method and term based method are complementary for each other and higher recall can be achieved by combining the above two types of models based on
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查看完整版本: Titlebook: Information Retrieval; 24th China Conferenc Shichao Zhang,Tie-Yan Liu,Chenliang Li Conference proceedings 2018 Springer Nature Switzerland