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2013 | OriginalPaper | Buchkapitel

27. An Entity Answer Ranking Method Based on MLNs

verfasst von : Fangqiong Chen, Zhengtao Yu, Jianyi Guo, Tao Shen, Yantuan Xian

Erschienen in: Proceedings of 2013 Chinese Intelligent Automation Conference

Verlag: Springer Berlin Heidelberg

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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.

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Metadaten
Titel
An Entity Answer Ranking Method Based on MLNs
verfasst von
Fangqiong Chen
Zhengtao Yu
Jianyi Guo
Tao Shen
Yantuan Xian
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-38466-0_27

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