文章基本信息

标题：Structural, Transitive and Latent Models for Biographic Fact Extraction
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作者：Nikesh Garera ; David Yarowsky
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：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.