@inproceedings{78f17206da75455cb6ce63d852f8033c,
title = "A maximum ENTROPY Markov model for chunking",
abstract = "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.",
keywords = "Chunking, Feature Template, Maximum Entropy Markov Model, Smoothing Algorithm",
author = "Sun, \{Guang Lu\} and Guan, \{Y. I.\} and Wang, \{Xiao Long\} and Jian Zhao",
year = "2005",
doi = "10.1109/ICMLC.2005.1527594",
language = "英语",
isbn = "078039092X",
series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",
publisher = "IEEE Computer Society",
pages = "3761--3765",
booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",
address = "美国",
note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",
}