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

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書(shū)目名稱Information Retrieval影響因子(影響力)




書(shū)目名稱Information Retrieval影響因子(影響力)學(xué)科排名




書(shū)目名稱Information Retrieval網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Information Retrieval網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Information Retrieval被引頻次




書(shū)目名稱Information Retrieval被引頻次學(xué)科排名




書(shū)目名稱Information Retrieval年度引用




書(shū)目名稱Information Retrieval年度引用學(xué)科排名




書(shū)目名稱Information Retrieval讀者反饋




書(shū)目名稱Information Retrieval讀者反饋學(xué)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