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- W2912343103 abstract "Result ranking diversification has become an important issue for web search, summarization, and question answering. For more complex questions with multiple aspects, such as those in community-based question answering (CQA) sites, a retrieval system should provide a diversified set of relevant results, addressing the different aspects of the query, while minimizing redundancy or repetition. We present a new method, DRN , which learns novelty-related features from unlabeled data with minimal social signals, to emphasize diversity in ranking. Specifically, DRN parameterizes question-answer interactions via an LSTM representation, coupled with an extension of neural tensor network, which in turn is combined with a novelty-driven sampling approach to automatically generate training data. DRN provides a novel and general approach to complex question answering diversification and suggests promising directions for search improvements." @default.
- W2912343103 created "2019-02-21" @default.
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- W2912343103 date "2019-05-13" @default.
- W2912343103 modified "2023-09-27" @default.
- W2912343103 title "Learning Novelty-Aware Ranking of Answers to Complex Questions" @default.
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- W2912343103 doi "https://doi.org/10.1145/3308558.3313457" @default.
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