Most commonly used alignment models, such as the IBM models and HMM-based aligner are unsupervised learners, and can only capture simple distortion features and lexical translational features due to the high complexity of the structure prediction space.
the CRFbased NER models are trained on manually annotated data, and admit richer sequence and lexical features.