Named Entity Recognition for Vietnamese Documents Using Semi-supervised Learning Method of CRFs with Generalized Expectation Criteria

Named Entity Recognition (NER) is an important, useful task in many natural language processing applications and much previous work in NER has been done in many other languages such as English, Japanese, Chinese However, Vietnamese NER task is still relatively new and challenge due to the characteristics of Vietnamese, the lack of a large annotated corpus This paper presents a new approach for Vietnamese NER -- a semi-supervised training method for Conditional random fields (CRFs) models using generalized expectation criteria to express a preference for parameter settings. We perform several experiments using different feature setting and different training data to show the high performance of this method and compare to the other method.