Recognizing Chinese Proper Nouns with Transformation-Based Learning and Ontology

This paper proposes an approach based on the Ontology and transformation-based error-driven learning (TBL) to recognize Chinese proper nouns. Firstly, our approach redefines the label set and tags Chinese words according to the usage of proper nouns and their context, and then it extracts Characteristic Information (CI) of the proper noun from the text and merges them based on the Ontology. Secondly, it tags the training corpus following the new definition of Multi-dimension Attribute Points (MAP), and then extracts rules using the TBL approach. Finally, it recognizes proper nouns by utilizing the rule set and Ontology. The experimental results in our open test show that the precision is 92.5% and the recall is 86.3%.