Soft-constrained inference for Named Entity Recognition
Named Entity Recognition is addressed by constraining inference in CRF. An two phases integer linear programming approach is proposed. Complex relationships among labels are automatically extracted from data. Extracted relationships are introduced as soft constraints in the ILP formulation. The proposed method significantly outperforms the state of the art approach.
named entity recognition integer linear programming conditional random fields rule extraction