Hence, we introduce posterior regularization (PR) (Ganchev et al., 2010) into our HMM inference algorithm.
(1) Based on previous work in the area (Tjong Kim Sang and De Meulder, 2003;McCallum and Li, 2003;Sha and Pereira, 2003), we begin with a standard set of features commonly used for training NER systems: given the paucity of training data, these features are numerous and sparse, we do not expect the CRF model to perform well with standard features alone, even with L1 regularization.