Chinese Sentence Parsing Based on Maximum Entropy Model
Zhongwei Guo · Dianzi xuebao · 2003
The shallow parsing theory is applied to partition Chinese sentence parsing into three procedures:TAG,CHUNK,BUILD and CHECK.To resolve the problem of lacking feature types for available probabilistic models and make the best of useful information for parsing in context,we present probabilistic model based on maximum entropy to evaluate the probability of each action in the parsing procedures.In this model,any useful information for parsing in a context could be an actual feature; the features and training events are defined; the strategy of feature selection and the algorithm of parameter estimation based on Generalized Iterative Scaling(GIS)are given; The final result of parsing is the parse tree with the largest probability searched with Breadth-first search(BFS).The model is experimentally proved satisfying in both parsing efficiency and precision.