Chinese maximal noun phrase parsing based on cascaded conditional random fields

This paper proposes an approach for Chinese maximal noun phrase parsing based on cascaded conditional random fields. In this approach, the parse tree of Chinese maximal noun phrase is constructed layer by layer. The Chinese chunks are first recognized by the lower conditional random fields model, then the result is passed as input to the higher model for recognition of phrases, the process of recognizing phrases is continued until no new phrases are discovered. Post-processing rules are constructed between the lower and higher models to modify the erroneous recognition of Chinese chunks, and finally the phrase structure tree of the Chinese maximal noun phrase is constructed. In open test, our Chinese maximal noun phrase parser achieves F1-score of 92.02%.