Resolution of Data Sparseness in Named Entity Recognition Using Hierarchical Features and Feature Relaxation Principle

This paper introduces a Mutual Information Independence Model (MIIM) and proposes a feature relaxation principle to resolve the data sparseness problem in MIIM-based named entity recognition via hierarchical features. In this way, a named entity recognition system with better performance and better portability can be achieved. Evaluation of our system on MUC-6 and MUC-7 English named entity tasks achieves F-measures of 96.1% and 93.7% respectively. It also shows that 20K words of training data would have given the performance of 90 percent with the hierarchical structure in the features compared with 30K words without the hierarchical structure in the features. This suggests that the hierarchical features provide a potential for much better portability.