Year: 2009
Type: conference-paper
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.
Authors: Nikesh Garera, David Yarowsky
Institution: Johns Hopkins University
Language: English
Topic: Web Data Mining and Analysis
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2043072004