Chinese Named Entity Recognition with Conditional Probabilistic Models

Abstract

This paper describes the work on Chinese named entity recognition performed by Yahoo team at the third International Chinese Language Processing Bakeoff. We used two conditional probabilistic models for this task, including conditional random fields (CRFs) and maximum entropy models. In particular, we trained two conditional random field recognizers and one maximum entropy recognizer for identifying names of people, places, and organizations in unsegmented Chinese texts. Our best performance is 86.2% F-score on MSRA dataset, and 88.53% on CITYU dataset.