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- W7072919 abstract "For the characteristics of factoid and list answers in domain Q&A system, we built a ranking model combined with multiple features of domain entity answers based on MLNs. This method uses predicate formulas to describe the relevant features of the questions–candidate answers and the answers–knowledge base, merging these features into Markov Logic Network, and then adopting discriminant training learning algorithm to learn the weights of feature parameters, which can give different weights according to the relevance of different features, finally it use MC-SAT algorithm reasoning to get the relevance of questions and answers, realize the answer ranking. Experiments show that the proposed method can greatly improve the answer precision and recall rates compared with other methods." @default.
- W7072919 created "2016-06-24" @default.
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- W7072919 date "2013-01-01" @default.
- W7072919 modified "2023-09-30" @default.
- W7072919 title "An Entity Answer Ranking Method Based on MLNs" @default.
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- W7072919 doi "https://doi.org/10.1007/978-3-642-38466-0_27" @default.
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