A maximum entropy Markov model for chunking
Guanglu Sun, Yi Guan, Xiaolong Wang, Jian Min Zhao · 2005
This paper presents a new chunking method based on maximum entropy Markov models (MEMM). MEMM is described in detail that combines transition probabilities and conditional probabilities of states effectively. The conditional probabilities of states are estimated by maximum entropy (ME) theory. The transition probabilities of the states are estimated by N-gram model in which interpolation smoothing algorithm is utilized on the basis of analyzing chunking spec. Experiment results show that this approach achieves an impressive performance: 92.53% in F-score on the open data sets of CoNLL-2000 shared task. The performance of the algorithm is close to the state-of-the-art.