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- W2794498049 abstract "Entity relation extraction is one of the most important tasks of information extraction. In the process of semi-supervised relation extraction, how to add thereliable relation patterns to the seed set is one of the key factors that determines the reliability of the relation classification. Aiming at this problem, a novel kernel function based on the shortest dependency tree kernel functionis proposed in this paper, which greatly improves the reliability of the new joining patterns and effectively alleviates the problem of semanticdrift in the semi-supervised relation extraction,making the results better in subsequent predictions. The experimental result shows that the proposed method is superior to the traditional kernel function method, the recall rate and F1 value are greatly improved." @default.
- W2794498049 created "2018-04-06" @default.
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- W2794498049 date "2017-11-07" @default.
- W2794498049 modified "2023-10-10" @default.
- W2794498049 title "A Pattern Learning Method Based on Kernel Function" @default.
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- W2794498049 doi "https://doi.org/10.1145/3158233.3159317" @default.
- W2794498049 hasPublicationYear "2017" @default.
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