A Study On Chinese Chunk Parsing

Syntactic parsing is an important and difficult task in the natural language processing (NLP). Because of the difficulties of complete syntactic parsing, chunk parsing has become an interesting alternative to full parsing. Using the divide-and-conquer strategy, syntactic parsing is divided into two sub-tasks, chunk parsing and the relationship analysis. The main goal of this paper is to implement Chinese chunk parsing task based on Morpho-Analysis, and provide the basis for complete syntactic parsing and other NLP tasks.In this paper, we first introduce the current research state of the chunk parsing and its significance. Based on the definition of chunk and the work of other researchers, we give the definition of Chinese chunks. Two systems for chunk parsing are built based on the Specialized Hidden Markov Model and Support Vector Machine Model.According to the different contextual information, we build five Specialized HMMs for Chinese chunk parsing. Via the analysis of the characteristic information from the chunks which have been tagged, we choose the different combination of characteristic information and classification means to realize the SVM models. Moreover, an error-driven learning approach is adopted to improve the chunk parsing results of Specialized HMM and SVM model.The models used in this paper are effective, and the experimental results show that the accuracies and recalls of chunking are satisfactory. The F-values of Specialized HMM and SVM chunking results are respectively 84.99% and 89.75%. With the help of error-driven learning, the performances of Specialized HMM-based chunking and SVM-based chunking are improved by 1.05% and 0.66%.The chunk parsing approaches introduced in this paper could be used in actual MT system, which can simplify sentences' structure and improve the holistic performance. In addition, the research of this paper would also be applied to other NLP tasks, such as information retrieval, text classification and so on.