This paper adopts three methods including Error-Driven,Support Vector Machines and Hidden Markov Model to recognize noun phrases in Chinese texts.

Using three methods of error-driven,support vector machine and hidden markov model,noun phrase recognition is carried on to chinese text,through comparative analysis to experiment,the results indicate that the recognition effects of SVM and HMM are overall better than the method of error-driven,HMM method has the distinct advantage in the closed test.

This paper proposes a hybrid error-driven combination approach to chunking Chinese Base noun phrase(Chinese Base NP),which combines TBL(Transformation-based Learning) model and CRF(Conditional Random Field) model.

This paper proposes a new method for recognizing the extents of the time expressions based on dependency parsing and error-driven learning,which begins with time trigger word(namely,the syntactic head of dependency relation),uses Chinese dependency parsing to recognize the extents of the time expressions,Subsequently,we use the transformation-based error-driven lear.

This paper proposes a chunking approach that combines support vector machine with error-driven learning.

This paper proposes a new method for recognizing the extents of the time expressions based on the dependency parsing and error-driven learning,which begins with time trigger word(namely,the syntactic head of dependency relation),uses Chinese dependency parsing to recognize the extents of the time expressions,and greatly improves the system performance;Subsequently,we .