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- W2043072004 abstract "This paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent properties of biographical data. The ensemble of these proposed models substantially outperforms standard pattern-based biographic fact extraction methods and performance is further improved by modeling inter-attribute correlations and distributions over functions of attributes, achieving an average extraction accuracy of 80% over seven types of biographic attributes." @default.
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- W2043072004 date "2009-01-01" @default.
- W2043072004 modified "2023-10-16" @default.
- W2043072004 title "Structural, transitive and latent models for biographic fact extraction" @default.
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- W2043072004 doi "https://doi.org/10.3115/1609067.1609100" @default.
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