A MAXIMUM ENTROPY CHUNKING MODEL WITH N-FOLD TEMPLATE CORRECTION

摘要

This letter presents a new chunking method based on Maximum Entropy (ME) model with N-fold template correction model. First two types of machine learning models are described. Based on the analysis of the two models, then the chunking model which combines the profits of conditional probability model and rule based model is proposed. The selection of features and rule templates in the chunking model is discussed. Experimental results for the CoNLL-2000 corpus show that this approach achieves impressive accuracy in terms of the F-score: 92.93%. Compared with the ME model and ME Markov model, the new chunking model achieves better performance.