Conditional random fields (CRFs) (Lafferty et al., 2001) further improve the performance (Kudo et al., 2004;Shi and Wang, 2007) by performing whole-sequence normalization to avoid label-bias and length-bias problems.
cRF-based algorithms typically require longer training times, and we observed an infeasible convergence time for our hybrid model.