Parsing named entity as syntactic structure

Named entity recognition (NER) plays an important role in many natural language processing applications. This paper presents a novel approach to Chinese NER. It differentiates from most of the previous approaches mainly in three respects. First of all, while previous work is good at modeling features between observation elements, our model incorporates syntactic structure as higher level information. It is crucial for recognizing long named entities, which are one of the main difficulties of NER. Secondly, NER and syntactic analysis have been modeled separately in natural language processing until now. We integrate them in a unified framework. It allows the information from each type of annotation to improve performance on the other, and produces the consistent output. Finally, few studies have been reported on the recognition of nested named entities in Chinese. This paper presents a structured prediction model for Chinese nested named entity recognition. Our approach have been implemented through a joint representation of syntactic and named entity structures. We have provided empirical evidence that parsing model can utilize syntactic constraints for recognizing named entities, and exploit the composition patterns of named entities. Experiment results demonstrate the mutual benefits for each task and output syntactic structure of named entities.