Paper: Structural Transitive and Latent Models for Biographic Fact Extraction

ACL ID E09-1035
Title Structural Transitive and Latent Models for Biographic Fact Extraction
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

This paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent proper- ties of biographical data. The ensem- ble of these proposed models substantially outperforms standard pattern-based bio- graphic fact extraction methods and per- formance is further improved by modeling inter-attribute correlations and distribu- tions over functions of attributes, achiev- ing an average extraction accuracy of 80% over seven types of biographic attributes.