@inproceedings{2523093f7def45a5a3f2805bbb1d00a6,
title = "Applying a multitask feature sparsity method for the classification of semantic relations between nominals",
abstract = "This paper extracts seven effective feature sets and reduces them to same dimension by principle component analysis (peA), such that it can utilize a multitask feature sparsity approach to the automatic identification of semantic relations between nominals in English sentences under maximum entropy discrimination (MED) framework. This method can make full use of related information between different semantic classifications to perform multitask discriminative learning and don't employ additional knowledge sources. At SemEval 2007, our system achieved a F-score of 69.15 \% which is higher than that by independent SVM.",
keywords = "Maximum entropy discrimination, Multitask learning, Semantic relation, Support vector machine",
author = "Guoqing Chao and Shiliang Sun",
year = "2012",
doi = "10.1109/ICMLC.2012.6358889",
language = "英语",
isbn = "9781467314855",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "72--76",
booktitle = "Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012",
address = "美国",
note = "2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 ; Conference date: 15-07-2012 Through 17-07-2012",
}