Chinese Noun Phrases Chunking: A Latent Discriminative Model with Global Features

In the fields of Chinese natural language processing, recognizing simple and non-recursive base phrases is an important task for natural language processing applications, such as information processing and machine translation. In stead of rule-based model, we adopt the statistical machine learning method, newly proposed Latent semi-CRF model to solve the Chinese noun phrase chunking problem. The Chinese base phrases could be treated as the sequence labeling problem, which involve the prediction of a class label for each frame in an unsegmented sequence. The Chinese noun phrases have sub-structures which could not be observed in training data. We propose a latent discriminative model called Latent semi-CRF(Latent Semi Conditional Random Fields), which incorporates the advantages of LDCRF(Latent Dynamic Conditional Random Fields) and semi-CRF that model the sub-structure of a class sequence and learn dynamics between class labels, in detecting the Chinese noun phrases. Our results demonstrate that the latent dynamic discriminative model compares favorably to Support Vector Machines, Maximum Entropy Model, and Conditional Random Fields(including LDCRF and semi-CRF) on Chinese noun phrases chunking.