For a fair comparison, we prepared L 1 -regularized supervised learning algorithms, which try to reduce the non-zero parameters in a model.
we utilized L 1 -regularized CRF (L1CRF) optimized by OWL-QN (Andrew and Gao, 2007) for NER, and the online structured output learning version of FOBOS (Duchi and Singer, 2009;Tsuruoka et al., 2009) with L 1regularization (ostL1FOBOS) for dependency parsing.