Chinese shallow parsing using different phase systems

We present two different shallow parsing methods that use language independent features. The first system does not split shallow parsing into any sub-tasks, and it is called one-phase shallow parsing system. The second one is called two-phase system that decomposes shallow parsing into two main sub-tasks, recognition (chunk) and classification (chunk), which are performed sequentially and independently with separate modules. Both systems are machine learning based systems, making the use of SVM classifiers. The main focus of this paper is to investigate the impact of different methods to the performance of shallow parsing. Experimental results show that one-phase method is quite effective; in fact it yields better performance between the two methods. Although each individual model was quite strong to begin with, we found that the two-phase method actually degraded the performance, or brought zero improvement. Although more sophisticated linguistic features are helpful, we use the same features in two shallow parsing methods.