Bootstrapping for Named Entity Tagging Using Concept-based Seeds 
 A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE , e.g. he/she/man/woman for PERSON NE . The bootstrapping procedure is implemented as training two successive learners . First, decision list is used to learn the parsing-based NE rules . Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner . The resulting NE system approaches supervised NE performance for some NE types . 