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- W2928189833 abstract "The retrieval of relevant documents by search engine mostly depends on how the users construct their queries. But in reality, most of the search queries received by search engines are either underspecified or ambiguous. Search result diversification is among the sensible solutions to disambiguate a search query by returning a ranked list of documents covering many aspects of the query topics as possible. This paper proposes a novel approach considering user query intent as a hierarchy. Specifically, we employed Recurrent Neural Networks (RNN) encoder-decoder attention mechanism to construct a diverse ranked list of documents. The model computes the relevance of each document in relation to its subtopics and compares it with previously selected documents in the rank list to ensure that its information is not covered by the previously selected documents. The experimental results, obtained by applying the proposed framework on Clueweb09 datasets, show that the proposed solution outperforms most of the existing methods." @default.
- W2928189833 created "2019-04-11" @default.
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- W2928189833 date "2019-03-01" @default.
- W2928189833 modified "2023-09-23" @default.
- W2928189833 title "Search Results Diversification based on Subtopics Attention Network" @default.
- W2928189833 doi "https://doi.org/10.1109/c-code.2019.8681009" @default.
- W2928189833 hasPublicationYear "2019" @default.
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