@inproceedings{f21368c535024fb9b88d66c1b07ed70f,
title = "Shallow parsing with Hidden Markov Support Vector Machines",
abstract = "Shallow parsing system, providing natural part syntactic information statement, to meet a lot of language information processing requirements, has received much attention recent years. Hidden Markov Support Vector Machines (HM-SVMs) for sequence labeling offer advantages over both generative models like HMMs and classifying models like SVMs which give labeling result for each positionseparately. We show how to train a HM-SVM model to achieve good performance on the data set of CoNLL2000 share task. The HM-SVMs yields an F-score of 95.51\% which is better than any system result of ConLL2000 share task.",
keywords = "Chunk, HM-SVMs, Shallow parsing",
author = "Fan, \{Shi Xi\} and Chen, \{Li Dan\} and Xuan Wang and Tang, \{Bu Zhou\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",
year = "2014",
month = jan,
day = "13",
doi = "10.1109/ICMLC.2014.7009716",
language = "英语",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "827--830",
booktitle = "Proceedings of 2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014",
address = "美国",
}