Chinese Named Entity Recognition with CRFs: Two Levels

Named entity recognition (NER) is one of the key techniques in natural language processing tasks such as information extraction, text summarization and so on. Chinese NER is more complicated and difficult than other languages because of its characteristics. This paper investigates Chinese named entity recognition based on CRFs, and implements three main named entities, person, location, and organization recognition in two levels: word level and character level. Experiments are made to compare the two level models¿ performances. In the experiments, different training scales and feature sets are utilized to look into the models¿ relationships with training corpus and their ability in making use of different features.