Automatically Selected Skip Edges in Conditional Random Fields for Named Entity Recognition
Incorporating distant information via manually selected skip chain templates has been shown to be beneficial for the performance of conditional random field models in contrast to a simple linear chain based structure (Sutton and McCallum, 2007; Galley, 2006; Liu et al., 2010). The set of properties to be captured by a template is typically manually chosen with respect to the application domain. In this paper, a search strategy to find meaningful skip chains independent from the application domain is proposed. From a huge set of potentially beneficial templates, some can be shown to have a positive impact on the performance. The search for a meaningful graphical structure demonstrates the usefulness of the approach with an increase of nearly 2% F1 measure on a publicly available data set (Klinger et al., 2008).