An algorithm combining statistics-based and rules-based for chunk identification of Chinese sentences

Natural language processing (NLP) is a very hot research domain. One important branch of it is sentence analysis, including Chinese sentence analysis. However, currently, no mature deep analysis theories and techniques are available. An alternative way is to perform shallow parsing on sentences which is very popular in the domain. The chunk identification is a fundamental task for shallow parsing. The purpose of this paper is to characterize a chunk boundary parsing algorithm, using a statistical method combining adjustment rules, which serves as a supplement to traditional statistics-based parsing methods. The experimental results show that the model works well on the small dataset. It will contribute to the sequent processes like chunk tagging and chunk collocation extraction under other topics etc.