Titlebook: Information Retrieval; 24th China Conferenc Shichao Zhang,Tie-Yan Liu,Chenliang Li Conference proceedings 2018 Springer Nature Switzerland

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书目名称Information Retrieval影响因子(影响力)




书目名称Information Retrieval影响因子(影响力)学科排名




书目名称Information Retrieval网络公开度




书目名称Information Retrieval网络公开度学科排名




书目名称Information Retrieval被引频次




书目名称Information Retrieval被引频次学科排名




书目名称Information Retrieval年度引用




书目名称Information Retrieval年度引用学科排名




书目名称Information Retrieval读者反馈




书目名称Information Retrieval读者反馈学科排名

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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.

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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.

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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.

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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